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Note: If you haven’t already read my previous post, I’d recommend you give it a quick scan as I cover some material which I make reference to below.

So you’ve got some access to AI tools and sort of know how they work. But what are they for? I know sometimes big tech can meet education with a solution looking for a problem and I’m keen to be clear-eyed about how we review “innovation”. I think there are some genuine use cases which I’ll outline a bit below. It’s worth noting that engagement with AI tech is deceptively simple. You can just write a question and get an (uncannily good sounding) answer. However, if you put in some time to craft your interaction, you’ll find that the quality rises sharply. Most people don’t bother, but I think that in academia we have enough bespoke situations that this might be warranted. In this article I’ll also detail a bit of the learning and investment of time that might be rewarded for each scenario. Here are, as I see them, some of those use caess:

1. Transcribe audio/video

AI tools like whisper-AI, which can be easily self-hosted with a fairly standard laptop, enable you to take a video or audio file and convert it very quickly to very accurate text. It’s accurate enough that I think the days of qualitative researchers paying for transcription are probably over. There are additional tools being crafted which can separate text into appropriate paragraphs and indicate specific speakers on the transcript (person 1, person 2, etc.). I think that it’s faster for most of us to read / skim a transcript, but also, for an academic with some kind of hearing or visual impairment, this is an amazingly useful tool. See: MacWhisper for a local install you can run on your Mac, or a full-stack app you can run as a WebUI via docker in Whishper (formerly FrogBase / whisper-ui).

Quick note: the way that whisper has been developed makes it very bad at distinguishing separate speakers, so development work is quite actively underway to add on additional layers of analysis which can do this for us. You can get a sense of the state of play here: https://github.com/openai/whisper/discussions/264. There are a number of implementations which supplement whisper-ai with pyannote-audio, including WhisperX and whisperer. I haven’t seen a WebUI version yet, but will add a note here when I see one emerge (I think this is underway with V4 of whishper (https://github.com/pluja/whishper/tree/v4). Good install guide here: https://dmnfarrell.github.io/general/whisper-diarization.

2. Summarise text

Large language models are very good at taking a long chunk of text and reducing it to something more manageable. And  it is reasonably straight-forward to self-host this kind of service using one of the 7B models I mentioned in the previous host. You can simply paste in the text of a transcript produced by whisper and ask a Mistral-7B model to summarise it for you using LMStudio without too much hassle. You can ask things like, “Please provide a summary of the following text: <paste>”. But you might also benefit from different kinds of presentation, and can add on additional instructions like: “Please provide your output in a manner that a 13 year old would understand” or “return your response in bullet points that summarise the key points of the text”. You can also encourage more analytical assessment of a given chunk of text, as, if properly coaxed, LLMs can also do things like sentiment analysis. You might ask: “output the 10 most important points of the provided text as a list with no more than 20 words per point.” You can also encourage the model to strive for literal or accurate results: “Using exact quote text from the input, please provide five key points from the selected text”. Because the underlying data that LLMs are trained on is full of colloquialisms, you should experiment with different terms: “provide me with three key hot takes from this essay” and even emojis. In terms of digital accessibility, you should consider whether you find it easier to get information in prose or in bulleted lists. You can ask for certain kinds of terms to be highlighted or boldface.

All of this work writing out questions in careful ways to draw out more accurate or readable information is referred to by experts as prompt engineering, and there is a lot of really interesting work being done which demonstrates how a carefully worded prompt can really mobilise an AI chatbot in some impressive ways. To learn more about prompt engineering, I highly recommend this guide: https://www.promptingguide.ai.

It’s also worth noting that the questions we bring to AI chatbots can also be quite lengthy. Bear in mind that there are limits on the number of tokens an AI can take in at once (e.g. the context length), often limited to around 2k or 4k words, but then you can encourage your AI chatbot to take on personality or role and set some specific guidelines for the kind of information you’d like to receive. You can see a master at work on this if you want to check out the fabric project. One example is their “extract wisdom” prompt: https://github.com/danielmiessler/fabric/blob/main/patterns/extract_wisdom/system.md.

You can also encourage a chatbot to take on a character, e.g. be the book, something like this:

System Prompt:


You are a book about botany, here are your contents:
<context>

User Query: "What are you about?"

There are an infinite number of combinations of long-form prose, rule writing, role-playing, custom pre-prompt and pre-fix/suffix writing which you can combine and I’d encourage people to play with all of these things to get a sense of how they work and develop your own style. It’s likely that the kid of flow and interaction you benefit from is quite bespoke, and the concept of neurodiversity encourages us to anticipate that this will be the case.

There are some emerging tools which do transcription, diarisation of speakers and summarisation in real time, like Otter.AI. I’m discouraged by how proprietary and expensive (e.g. extractive) these tools are so far, and I think there’s a quite clear use case for Universities to invest time and energy, perhaps in a cross-sector way, to develop some open source tools we can use with videoconferencing, and even live meetings, to make them more accessible to participation from staff with sensory sensitivies and central auditory processing challenges.

3. Getting creative

One of the hard things for me is often the “getting started” part of a project. Once I’m going with an idea (provided I’m not interrupted, gasp!) I can really move things along. But where do I start? Scoping can stretch out endlessly, and some days there just isn’t extra energy for big ideas and catalysts for thinking. It’s also the case that in academia we increasingly have less opportunities for interacting with other scholars. On one hand this is because there might not be others with our specialisation at a given university and we’re limited to conferences to have those big catalytic converastions. But on the other hand, it’s possible that the neoliberalisation of the University and marketisation of education has stripped out the time you used to have for casual non-directed converastions. On my campus, even the common areas where we might once have sat around and thought about those things are also gone. So it’s hard to find spaces, time and companions for creativity. Sometimes all you’ve got is the late hours of the night and you realise there’s a bit of spare capacity to try something out.

The previous two tasks are pretty mechanical, so I think you’ll need to stick with me for a moment, but I want to suggest that you can benefit from an AI chatbot to clear the logjam and help get things flowing. LLMs are designed to be responsive to user input, they absorb everything you throw at them and take on a persona that will be increasingly companionable. There are fascinating ethical implications for how we afford agency to these digital personas and the valence of our relationships with them. But I think for those who are patient and creative, you can have a quite free-flowing and sympathetic conversation with a chatbot. Fire up a 7B model, maybe Mistral, and start sharing ideas and open up an unstructured converastion and see where it takes you. Or perhaps see if you can just get a quick list to start: “give me ten ideas for X”.

Do beware the underlying censorship in some models, especially if your research area might be sensitive, and consider drawing on models which have been fine-tuned to be uncensored. Consider doing some of the previous section work on your own writing: “can you summarise the key points in this essay?” “what are the unsubstantiated claims that might need further development?”

There’s a lot more to cover, but this should be enough to highlight some of the places to get started, and the modes of working with the tools which will really open up the possibilities. In my next post, I’ll talk a bit about LLM long-term memory and vector databases. If you’re interested in working with a large corpus of text, or having a long-winded conversation preserved across time, you might be interested in reading more!

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I’ve spent the last several months playing with AI tools, more specifically large language (and other adjacent data) models and the underlying corpus of data that formed them, trying to see if there are some ways that AI can help an academic like me. More particularly, I’m curious to know if AI can help neurodivergent scholars in large bureaucratic Universities make their path a bit easier. The answer is a qualified “yes”. In this article, I’ll cover some of the possible use cases, comment on the maturity, accessiblity and availability of the tech involved and explain some of the technological landscape you’ll need to know if you want to make the most of this tech and not embarrass yourself. I’ll begin with the caveats…

First I really need to emphasise that AI will not fix the problems that our organisations have with accessiblity – digital or otherwise – for disabled staff. We must confront the ways that our cultures and processes are founded on ableist and homogenous patterns of working, dismantle unnecessary hierarchies, and reduce gratuitous beurocracy. Implementing AI tools on top of these scenarios unchanged will very likely intensify vulnerability and oppression of particular staff and students and we have a LOT of work to do in the modern neoliberal University before we’re there. My worst case scenario would be for HR departments to get a site license to otter.AI and fire their disability support teams. This is actually a pretty likely outcome in practice given past patterns (such as the many University executives which used the pandemic as cover to implement redundancies and strip back resource devoted to staff mental health support). So let’s do the work please? In the meantime, individual staff will need to make their way as best as they can, and I’m hoping that this article will be of some use to those folx.

The second point I need to emphasise at the outset is that AI need not be provided through SAAS or other-subscription led outsourcing.Part of my experimentation has been about tinkering with open source and locally hosted models, to see about whether these are a viable alternative to overpriced subscription models. I’m happy to say that “yes”! these tools are relatively easy to host on your own PC, provided it has a bit of horsepower. Even more, there’s no reason that Universities can’t host LLM services on a local basis at very low cost per end user, vastly below what many services are charging like otter.AI’s $6/mo fee per user. All you need is basically just a bank of GPUs, a server, and electricity required to run them.

What Are the Major Open Source Models?

There are a number of foundational AI models. These are the “Big Ones” created at significant cost running over billions of data points, by large tech firms like OpenAI, Microsoft, Google, Meta etc. It’s worth emphasising that cost and effort are not exclusively bourne by these tech firms. All of these models are generated on the back of freely available intellectual deposit of decades of scholarly research into AI and NLP. I know of none which do not make copious use of open source software “under the hood.” They’re all trained on data which the general public has deposited and curated through free labour into platforms like wikipedia, stackexchange, youtube, etc., and models are developed in public-private partnerships with a range of University academics whose salaries are often publicly funded. So I think there is a strong basis for ethically oriented AI firms to “share alike” and make their models freely available, and end users should demand this. Happily, there have been some firms which recognise this. OpenAI has made their GPT1 and GPT2 models available for download, though GPT3 and 4 remain locked behind a subscription fee. Many Universities are purchasing GPT subscriptions implicitly as this provides the backbone for a vast number of services including Microsoft’s CoPilot chatbot, which have under deployment to University staff this last year as a part of Microsoft’s ongoing project to extract wealth from the education sector in the context of subscription fees for software (Microsoft Teams anyone?). But it doesn’t have to be this way – there are equally performant foundational models which have been made freely available to users who are willing to hack a bit and get them working. These include:

  • LLaMA (Language Learning through Multimodal Adaptation), a foundation model developed by Meta
  • Mistral (a foundation model designed for mathematical reasoning and problem-solving), which has been the basis for many other models such as NeuralChat by Intel.
  • Google’s Gemini and BERT models
  • BLOOM, developed by a consortium called BigScience (led by huggingface primarily)
  • Falcon, which has been funded by the Abu Dhabi sovereign wealth fund under the auspices of Technology Innovation Institute (TII)
  • Pythia by EleutherAI
  • Grok 1 developed by X.ai

These are the “biggies” but there are many more smaller models. You can train your own models on a £2k consumer PC, so long as it has a bit of horsepower and a strong GPU. But the above models would take, in some cases, years of CPU time for you to train on a consumer PC and have billions or even trillions (in the case of GPT4) parameters.

What Do I Need to Know About Models? What can I run on my own PC?

To get a much broader sense of how these models are made and what they are I’d recommend a very helpful and accessible write-up by Andreas Stöffelbauer. For now it’s worth focussing on the concept of “parameters” which reflects the complexity of the AI model.You’ll usually see this listed next to the model’s name, like Llama7B. And some models have been released with different parameter levels, 7B, 14B, 30B and so on. Given our interest in self-hosting, it’s worth noting that parameter levels are also often taken as a proxy for what kind of hardware is required to run the model. While it’s unlikely that any individual person is going to train a 30B model from scratch on their PC, it’s far more likely that you may be able to run the model after it has been produced by one of these large consortia that open source their models.

Consumer laptops with a strong GPU and 16GB of RAM can generally run most 7B parameter models and some 10G models. You’ll need 32GB of memory and a GPU with 16GB of VRAM to get access to 14B models, and running 30B or 70B models will require a LOT of horsepower, probably 24/40+ GB RAM which in some cases can only be achieved using a dual-GPU setup. If you want to run a 70B model on consumer hardware, you’ll need to dive the hardware discussion a bit as there are some issues that make things more complex in practice (like a dual-GPU setup), but to provide a ballpark, you can get second hand NVidia RTX 3090 GPU for £600-1000 and two of these will enable you to run 70B models relatively efficiently. Four will support 100B+ models which is veering close to GPT4 level work. Research is actively underway to find new ways to optimise models at 1B or 2B so that they can run with less memory and processing power, even on mobile phones. However, higher parameter levels can help with complex or long-winded tasks like analysing and summarising books, preventing LLM “hallucination” an effect where the model will invent fictional information as part of its response. I’ve found that 7B models used well can do an amazing range of tasks accurately and efficiently.

While we’re on the subject of self-hosting, it’s worth noting that when you attempt to access them models are also often compressed to make them more feasible to run on consumer hardware, using a form of compression called “quantization“. Quantization levels are represented with “Q” values, that is a Llama2 7B model might come in Q4, Q5 and Q8 flavours. As you’ll notice lower Q levels require less memory to run. But they’re also more likely to fail and hallucinate. As a general rule of thumb, I’d advise you stick with Q5 or Q6 as a minimum for models you run locally if you’re going to work with quantized models.

The units that large language models work with are called tokens. In the world of natural language processing, a token is the smallest unit that can be analyzed, often separated by punctuation or white space. In most cases tokens correspond to individual words. This helps to breaks down complex text into manageable units and enables things like part-of-speech tagging and named entity recognition. A general rule of thumb is that 130 tokens correspond to roughly 100 words. Models are trained to handle a maximum number of array elements, e.g. tokens in what is called the “context length“. Humans do this too – we work with sentences, paragraphs, pages of text, etc. We work with smaller units and build up from there. Context length limits have implications for memory use on the computers you use for an LLM, so it’s good not to go too high or the model will stop working. Llama 1 had a maximum context length of 2,024 tokens and Llama 2 stops at 4,096 tokens. Mistral 7B stops at 8k tokens. If we assume a page has 250 words, this means that Llama2 can only work with a chunk of data that is around 16 pages long. Some model makers have been pushing the boundaries of context length, as with GPT4-32K which aims to support a context length of 32K or about 128 pages of text. So if you want to have an LLM summarise a whole book, this might be pretty relevant.

There are only a few dozen foundational models available and probably only a few I’d bother with right now. Add in quantization and there’s a bit more to sift through. But the current end-user actually has thousands of models to sift through (and do follow that link to the huggingface database which is pretty stellar) for one important reason: fine-tuning.

As any academic will already anticipate, model training is not a neutral exercise. They have the biases and anxieties of their creators baked into them. In some cases this is harmless, but in other cases, it’s pretty problematic. It’s well known that many models are racist, given a lack of diversity in training data and carelessness on behalf of developers. They are often biased against vernacular versions of languages (like humans are! see my other post on the ways that the British government has sharpened the hazards of bias against vernacular English in marking). And in some other instances, models can produce outputs which veer towards some of the toxicity embedded in the (cough, cough, reddit, cough) training data used. But then attempts to address this by developers have presented some pretty bizarre results, like the instance of Google’s gemini model producing a bit too much diversity in an overcorrection that resulted in racially diverse image depictions of nazis. For someone like me who is a scholar in religion, it’s also worth noting that some models have been trained on data with problematic biases around religion, or conversely aversion to discussing it at all! These are wonderful tools, but they come with a big warning label.

One can’t just have a “redo” of the millions of CPU hours used to train these massive models, so one of the ways that developers attempt to surmount these issues is with fine-tuning. Essentially, you take the pre-trained model and train it a bit more using a smaller dataset related to a specific task. This process helps the model get better at solving particular problems and inflecting the responses you get. Fine-tuning takes a LOT less power than training models, and there are a lot of edge cases, where users have taken models after they’ve been developed and attempted to steer them in a new direction or a more focussed one. So when you have a browse on the huggingface database, this is why there aren’t just a couple dozen models to download but thousands as models like Mistral have been fine-tuned to do a zillion different tasks, including some that LLM creators have deliberately bracketed to avoid liability like offering medical advice, cooking LSD, or discussing religion. Uncensoring models is a massive discussion, which I won’t dive into here, but IMHO it’s better for academics (we’re all adults here, right?) to work with an uncensored version of a model which won’t avoid discussing your research topic in practice and might even hone in on some special interests you have. Some great examples of how censoring can be strange and problematic here and here.

Deciding which models to run is quite an adventure. I find it’s best to start with the basics, like llama2, mistral and codellama, and then extend outwards as you find omissions and niche cases. The tools I’ll highlight below are great at this.

There’s one more feature of LLMs I want to emphasise, as I know many people are going to want to work with their PDF library using a model. You may be thinking that you’d like to do your own fine-tuning, and this is certainly possible. You can use tools like LLaMA-Factory or axolotl to do your own fine-tuning of an LLM.

How Can I Run LLMs on My Pc?

There is a mess of software out there you can use to run LLMs locally.

In general you’ll find that you can do nearly anything in Python. LLM work is not as complex as you might expect if you know how to code a bit. There are amazing libraries and tutorials (like this set I’d highly recommend on langchain) you can access to learn and get up to speed fairly quickly working with LLMs in a variety of use-cases.

But let’s assume you don’t want to write code for every single instance where you use an LLM. Fair enough. I’ve worked with quite a wide range of open source software, starting with GPT4All and open-webui. But there are some better options available. I’ve also tried out a few open source software stacks, which basically create a locally hosted website you can use to interface with LLM models which can be easily run through docker. Some examples include Fooocus, InvokeAI and Whishper. The top tools “out there” right now seem to be:

Note: the most comprehensive list of open source tools you can use to run an AI chatbot I’ve seen to date can be found here.

I have a few tools on my MacBook now and these are the ones I’d recommend after a bit of trial and error. They are reasonably straight-forward GUI-driven applications with some extensability. As a starting point, I’d recommend lmstudio. This tool works directly with the huggingface database I mentioned above and allows you to download and keep models organised. Fair warning, these take a lot of space and you’ll want to keep an eye on your hard disks. LMStudio will let you fine tune the models you’re using in a lot of really interesting ways, lowering temperature for example (which will press the model for more literal answers) or raising the context length (see above). You can also start up an ad hoc server which other applications can connect to, just like if you were using the OpenAI API. Alongside LMStudio, I run a copy of Faraday which is a totally different use case. Faraday aims to offer you characters for your chatbots, such as Sigmund Freud or Thomas Aquinas (running on a fine-tuned version of Mistral of course). I find that these character AIs offer a different kind of experience which I’ll comment on a bit more in the follow-up post along with mention of other tools that can enhance this kind of AI agent interactivity like memgpt.

There are real limits to fine-tuning and context-length hacking and another option I haven’t mentioned yet, which may be better for those of you who want to dump in a large library of PDFs is to ingest all your PDF files into a separate vector database which the LLM can access in parallel. This is referred to as RAG (Retrieval-Augmented Generation). My experimenting and reading has indicated that working with RAG is a better way to bring PDF files to your LLM journey. As above, there are python ways to do this, and also a few UI-based software solutions. My current favourite is AnythingLLM, a platform agnostic open source tool which will enable you to have your own vector database fired up in just a few minutes. You can easily point AnythingLLM to LMStudio to use the models you’ve loaded there and the interoperability is pretty seamless.

That’s a pretty thorough introduction to how to get up and running with AI, and also some of the key parameters you’ll want to know about to get started. Now that you know how to get access up and running, in my second post, I’ll explain a bit about how I think these tools might be useful and what sort of use cases we might be able to bring them to.

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Every one of us is shadowed by an illusory person: a false self. This is the man that I want myself to be but who cannot exist, because God does not know anything about him. … My false and private self is the one who wants to exist outside the reach of God’s will and God’s love — outside of reality and outside of life. And such a life cannot help but be an illusion. … The secret of my identity is hidden in the love and mercy of God. … Therefore I cannot hope to find myself anywhere except in him. … Therefore there is only one problem on which all my existence, my peace and my happiness depend: to discover myself in discovering God. If I find Him, I will find myself, and if I find my true self I will find him Thomas Merton, New Seeds of Contemplation (pp. 34-36).

I’ve been reading David G. Benner’s book The Gift of Being Yourself for Lent this year. One of the key emphases of Benner’s account is the importance of self-knowledge as a companion to knowing God. For Benner, like many similar writers, these two quests – to know the self and to know God – are intertwined and interdependent. You cannot fully know youself in a solipsistic way, as this is not your true self, which is only revealed in intimate self-disclosing and vulnerable relationship with others. And similarly, you cannot know God without setting out on the first quest. In this he follows a long line of Christian spiritual theology including modern thinkers like Thomas Merton (quoted at the top), but also a wide range of historical spiritual theology as well, including intensely psychological thinkers like Augustine of Hippo, Thomas A’Kempis and Julian of Norwich. I’m mindful that theology can tend to veer in either of these two directions, struggling to hold them together. We find tomes elaborating knowledge of God in abstract (dare I say “analytical”) ways, with authors who fail to see any reason for self-disclosure (contra Augustine’s confessions). And the past decades are littered with spiritual leaders who offered a public profile based on personal narrative, a journey towards success and strength and supposed authenticity only to discover that this was an aspirational and ultimately untrue performance of self. I’m particularly fond of the work of thinkers like Teresa of Avila and Henri Nouwen as they do so much to emphasise the fragile, fallible self as a core feature of this spiritual path of self-discovery. We do not uncover truth in our strength, but in our frailty. This also excludes grandiose stories of overcoming weakness towards strength. The truth of our beloved creatureliness lies in our continued partiality, fallibility and desparate need for companionship.

But I’m also mindful that this journey into the self comes with different levels of complexity. Victims of trauma can often experience a darkening of the self where personal biographical memory can become fragmentary at best. The narratives in which our self-reckoning might be situated can be rendered inaccessible, sometimes permanently so. In a similar way, many neurodivergent persons are subjected to behaviouralist modes of conditioning when they are young, encouraged (sometimes harshly coerced) to “mask” and conceal their differences. This too can result in a concealment of the self from the self. Sometimes there is a negative reinforcement, with trauma colluding with training. When this is the case, as it has been for me, setting out on a journey to understand yourself is a recursive one, leading to a process by which one must work through a series of personas.

Many people never venture on this journey, as – though ultimately rewarding and important – it can be difficult, humiliating, and strenuous. It can sometimes be easier simply to live in the moment, making a go at a virtuous life which leaves our reactions, motives, and behaviours uninterrogated. For those who venture forth, there are rewards, opening yourself to new levels of transparency and awareness of your own frailty can enable deepened relationships and new forms of interpersonal care and mutual aid both with human and divine companions.

I wonder how our forms of spriritual care and empowerment scale towards the complex form of this journey. I’m not meaning to say that self-discovery is simple for anyone, but I think it is fair to say that for some of us it is complex at a level that passes a certain qualitative threshold, given the need to draw on levels of psychological energy and generate interpersonal precarity which can pose hazards to wellbeing. The journey can require heroic levels of energy and / or time, particularly if one is working recursively.

Benner stresses the point that the self can be found, that the journey is not endless, that knowing the self can be attained. I think this is not exclusive to, but nonetheless a unique feature of certain forms of spirituality including Christian spiritual practice. This uniqueness lies in the (at least in the context of orthodox Christian belief) assumption that (1) our self is irreducible, that is, unique only to a particular embodied subjectivity and not something which can be passed from one bodily form to another, or – ultimately – leave the body entirely and (2) in the conviction that this quest can have an outcome, that knowledge and perception of self is desirable, not to the extent of self-absorbtion or solipsism (which is certainly possible) but in a way that exists in dialectic with knowledge of “the other”. In the best instance, you may reach a level of authentic self-realisation which is a crucial part of the spiritual journey, and ultimately a factor in the health of the spiritual communities you are a part of. In stressing this point, however, I think Banner’s account could be augmented to attend to the ways that there are different forms of journey, some which potentially exist beyond a threshold of unknowing. Is there a unique valence to spiritual practices and experiences for people who have experienced this kind of complex subjectivity, for whom there is an experience of being unknown, even to yourself, in spite of seeking that knowledge? I think there might be, and am keen to unpack some of those dynamics in future writing. Stay tuned for more and in the meantime, I trust your own journey is going well.

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Forging and living in community is hard work. Anyone who tells you otherwise is either not aware of the suffering or hard work of other members in their community which is ongoing on their behalf or inexperienced. But this labour can also be part of the joy and beauty of it, a kind of craft taken up in the interest of establishing and maintaining spaces of loving mutual care for one another.

While people can sometimes tend to view church as a form of consumerised therapy, e.g. something that is undemanding (or only so demanding as to avoid revealing our interpersonal shortcomings) and meets a person’s (able-bodied) needs implicitly, there are other examples in recent years of reflection on ecclesia which focusses on areas where there is hard work to be done and towards the ways that we might sharpen the tools we bring to community-as-craft:

  • confronting racism and the need for forms of repentance and reconciliation as white ethno-nationalism has crept into particular instances of Christian self-identity
  • confronting institutional and interpersonal forms of misogyny and exclusion of women
  • addressing the ways that elitism and class (un)consciousness, in the form of wealth, literacy, or social status can subvert worship
  • unpacking the phenomenon of spiritual abuse and the ways that other more adjacent forms of trauma should be drawn into our reflection on life together
  • …and finally, assessing the ways that we have created spaces where the disabled God is unwelcome

As a theologian who occupies a small slice of these intersectional categories, I am wary of the ways that in some of these fields, reflection about justice can come in speculative forms – where you might imagine how it is for another person who is oppressed whilst not experiencing oppression yourself – rather than arising from lived experience or wisdom. I’m working on a book, God is Weird where I’ll unpack some of my own analysis as an autistic theologian and Christian ethicist and I’ve also begun setting up an ethnographic project around autistic spirituality. These projects won’t be complete for some years now, I expect, so in the meantime I thought it might be helpful to put down a series of posts on what resources I’ve found (some of them are written, but not all!), areas where I see opportunities for practitioners to change their approaches, and ways that our core theological and doctrinal thinking might need to change in light of the forms of exclusion of privilege which have narrowed theological reflection in the 20th century.

It is worth emphasising that the task here is quite severe: Christians have been at the forefront of conversion therapy and eugenics movements which have sought to eradicate and hide autistic lives, usually achieved through breathtaking levels of interpersonal violence. So while there is joy abounding in a neurodivergent theology, this is very much not a cheery lighthearted conversation for autists who are sharply aware of these legacies and hold them as personal trauma. For this post, I’m going to frame out some of the parameters that I think might be salient for autistic church for the sake of later reflection where I’ll unpack them, so here goes:

(1) Queering church: the starting point for all of this has to be an accounting for weirdness and unconventionality in forms of Christian life and worship, doctrine and collectivity. I note that sometimes neurodivergent (“ND”) thinkers might try to avoid getting caught up in debates around sexual orientation and attempt more covert or disentangled ways to engage this subject, but after a long season of covert work as a theologian, I’ve really begun to question the wisdown of such an approach. Queerness has always been about wider concerns beyond sexual orientation, but also “unconventional” sexual orientation and gender identity have always been a co-occuring feature of ND lives and bodies. As a result, I tend to view adjacent queering projects as complementary. Following on from this, there are some hard questions we need to ask in opening up a conversation about queering church for ND neighbours:

  • Why do we obsessively focus on categories of normality and flee from reflection on embodied difference?
  • Why have Christians led movements to eradicate or convert forms of embodied difference (like down syndrome or autism)? It is really important to reckon with the intensity of violence that some Christian communities have sustained towards disabled members of their communities. Christians were among those at the forefront of the eugenics movement in the 20th century and still are via the proxy of genetic research into “causes” of autism and gene therapies in the 21st century. But in other contexts, it is equally the case that conservative Christians have been at the forefront of pushing for access to behavioural therapies (like ABA) which seek to convert (I’m not exaggerating when I suggest we shoud substitute language here of “torture”) autistic children and young adults so that they conceal conspicuous behaviours. I know of many ND children who were pressed into narratives of “normal” behaviour as part of sunday school teaching. I know of no disavowal of ABA by any Christian denomination. As Laura MacGregor and Allen G. Jorgenson observe in one recent journal article, especially for parents and carers of children with disabilities, for a variety of reasons, churches feel “unsafe” with the consequence that those families “[withdraw] entirely from church”.
  • While the first-hand accounts of caregivers form an important body of knowledge which we should take very seriously in our reflection on the ways that churches exclude disabled persons, there is a hazard here in that these accounts of caregiver suffering and burnout can displace first-person accounts of chuch. I observe that much of the underwriting support for ABA and eugenics charities like Autism Speaks tends to be led by caregivers desperate for support which can ultimately cause further harm and marginalisation of those persons they are caring for. Why have we tended to focus on the (very real, to be fair) suffering of able-bodied caregivers and ignored the trauma and stolen agency of care-receivers?
  • I have also observed that conversations about accommodations and support start off very easy and meet hard limits very quickly. Too often, after a brief burst of enthusiasm (again, perhaps not appreciating the full scale of work that may be involved), we often tend to drift eventually towards a framing of conversations about forms of accommodation, conversations about rights and entitlements as a zero-sum game, emphasising limits to what is “practical” and in sharper cases supporting rearguard action to centre and celebrate privileged people who are ceding those “rights” for the sake of another person (e.g.  men’s rights, or white experiences of “racism”).

(2) There is an urgent need to centre embodied diversity as we reflect on life together. We’ve done a bit of thinking (thanks in no small part to the legacy of Nancy Eiesland) around accessibilty for wheelchair users in the built environment, but there are many other forms of inaccessiblity that don’t get attention, including some straight-forward aspects of ND embodied experience like sensory sensitivities around sound, light, texture and food. How do we worship corporately in ways that start from the assumption that auditory, visual, and sensorimotor differences are going to abound? Formulaic approaches to worship music aren’t likely to work. And how can we create pathways for people to share their negative experiences without fear of marginalisation, shame or sanction? Two autistic people in the same church may have conflicting experiences: loud music can produce suffering for some people whilst intense bodily vibration can generate deep joy for another. This is a paradox we haven’t spent much time exploring in the church or in academic theology. In a similar way, we’ve done a bit of work to accommodate some forms of hearing impairment with AV equipment, but other forms of neurological difference like central auditory processing disorder (CAPD) or just plain autistic cognition can lead to challenges around pace and flow of auditory information: a 30 minute long eloquent sermon may be a brilliant canvas for some people but a blank page for many others. Should the sermon or song really be the centerpiece of a church service? There are interesting questions about the love feast and other forms of liturgical meals which seem to have fallen off the radar as we seek to emulate para-ecclesial genres of oratory and performance.

(3) There is an obsession, especially within Protestant forms of worship on speech and oratory. This can often crowd out silence. Or worse-still this preference can lead to a commodification of silence, where it becomes seen as especially holy (mindfulness workshop anyone?) rather than part of someone’s mundane everyday experience. ND church needs to be able to accommodate and celebrate persons who are non-verbal or non-speaking (even if this is occasional rather than persistent as is often the case with masking autistic people). This is more common than you expect, especially given the ways we can tend to use invalidated pseudo-social scientific instruments like the Myers-Briggs tests to badge non-communication as a personality trait (and thus not requiring a person’s energy as attention as a prolific communicator). We also need to reflect on ways of accommodating different forms of speech, which may involve gesture, sounds, or echolalia. I was particularly struck by the account by Eli Clare (Brilliant Imperfection: Grappling with Cure) of having shaking and erratic body motions as a form of embodiment which can be celebrated. Think about the number of places across a worship service where perfection is upheld (even if not achieved) as a pursuit: music production, scholarly and eloquent but accessible sermons, clever spontaneous announcements. Would it feel perfectly normal if a person with cerebal palsy delivered a sermon in your church? So often we assume that effective communication is the problem of the communicator. But what if listening was sanctified as a challenging liturgical practice?

There’s more I’m sure, including thinking about how we can confront and dismantle ablist hierarchies, but that’s a good start for now. More from me on this in weeks to come!

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Another love letter to curious neurotypical allies

Another way that autism is often pathologised relates to characterisations of us as having a limited number of interests (sometimes described as “monotropism”), having “weak central coherence,” or as being inflexible or rigid around activities and conversations. More recently, autistic people have taken the reins of research and started to characterise this aspect of our cognition in different ways, particularly around the importance thinking through and tending to flow states and inertia. There is also some pretty interesting research emerging around cognitive difference around “predictive coding”.

Of course we can flip the pathologisation, asking why non-autistic people have to be so flighty, swapping from one topic to the next and correspondingly slow to complete tasks and notice connections across different domains. Of course this is just as unfair, but it highlights the ways that different forms of cognition are really just that: different.

We all experience times when we hit mental friction, e.g. trying to think through a problem in a way that just isn’t working, to such a degree that all your mental gears grind to a halt and you need to regroup. My ideal way of working through something is to work on tasks or topics, one at a time. I really want to understand and consolidate something (even if just provisionally, to highlight what has been left undone, to highlight what gaps in knowledge have been identified, what work might remain, etc.) before I move on. And I really like to set things up properly, getting the ambiance right, ensuring the right tools are ready, and the stage is set for effective work. For a simple thing like washing dishes, I want to make sure I’ve got a nice clear space for drying, a good soundtrack playing, and enough time set aside so that once I’ve begun, I can reasonably expect to finish the washing in one go. The upside of this is that once I’ve got things moving, I can go very fast – faster than most. But constantly stopping and starting, interrupting flow and having to hit the brakes and stop that inertia, can be really uncomfortable. That discomfort can be mitigated, when I anticipate I’m going to be interrupted, I’ll split tasks up into sub-groups, folding just the kids laundry and leaving towels for another day, or washing silverware and plates and stopping before I get to the pots.

It’s hard to convey the bodily sensation of having inertia stopped abrubtly, but it’s important to stress that it’s way beyond what you might think of as normal levels of uncomfortable annoyance. If I’m trapped in a situation where we are constantly stopping and starting, failing to take time to set up and define parameters, leaving things unfinished and never consolidating conversations or marking progress before moving on, it can feel like being forced to hold my breath or being exposed to loud music for hours at a time: uncomfortable, veering towards tortuous or traumatic. Poorly designed academic workshops can sometimes be like this or a dinner party with expectations that we all engage in small talk about topics that none of us actually care about (yes, ND folx are often not big fans of small talk). Again, I have ways of mitigating this – taking on the task of note-taker for a conversation and trying to consolidate the conversation for our small group; being the person at the party who gently invites “deep” conversations to those who are interested.

What I’d like you to know more about, however, is the degree to which our societies are attuned to non-autistic ways of working and being in the world. It’s not that (as often many people seem to assume) we’ve reached a nirvana where everyone can just do things at their own pace, but that people just don’t notice how they’re being given priority around the horizons that we set for default deadlines and planning, how often we change the accepted approach to a given form of practice, etc.

In my perfect world, I’d be able to focus days around specific forms of activity: maybe teaching classes on Monday and Tuesday, conducting research on Wednesday, holding meetings on Thursday morning and marking exams in the afternoon, etc. But that isn’t how things work out: my teaching activities are spread across the week and there are rules to enforce this kind of work scheduling. When I’ve requested a different approach, I’ve been told this would disadvantage other colleagues and is against “the rules”. My requests are characterised as special pleading or even suspicious. When I’ve asked for the terms of a meeting to be defined ahead of time, perhaps even collaboratively, colleagues can sometimes be defensive: “Why can’t we do this in a ‘relational’ way and just have a conversation? Why do you always need to email so much?”

The upside of this is that at the start of each day, I need to think my way through the number of interactions and tasks I’m going to be drawn into, and I try to consolidate for myself as much as possible. I calculate the people I need to work with who won’t be willing or able to accommodate a different pattern from their own. Those become the “triage” events that require special levels of energy and planning. Then I need to try and consolidate the rest of the time as best as I can, but it’s often the case that the triaging takes up *all* the time. The sad thing about this is that being flexible and accommodating of different patterns doesn’t just help autistic people like me – there are many other non-ND folx who need a bit of time to get “into gear” and might want to set up collaboratively designed ways of working to get around other invisible challenges.

If you’d like to learn more, I highly recommend:

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One of the key reasons I’m reluctant to share with others about being autistic relates to the way that communication by autistic people has been relentlessly pathologised. Even now, the key way that autism is defined in diagnostic manuals and social research primarily foregrounds, as one research article puts it is that: “autism manifests in communication difficulties, challenges with social interactions, and a restricted range of interests”. I don’t know a single autistic person who would foreground those things are the primary driver of their personal alterity and lived experience. They are challenges, but those aren’t the defining features of being autistic. But that’s the stereotype out there which is continually repeated by non-autistic researchers. This is foregrounded for those autists in Higher Education who declare a disability at work as we’re categorised in the following way by the Higher Education Statistics Agency: “A social/communication impairment such as Asperger’s syndrome / other autistic spectrum disorder.”

The upshot of this is that I have an abiding fear that when sharing about my neurodivergence with others, that person will subconsciously begin to find signs of disorder in every social interaction we have after that discovery. This has happened in the past and it’ll continue to happen in the future. And there are corrolaries which also make me wince, like when people speak really loudly or slowly to immigrants in spite of their clear English language proficiency. It’s very hard to surmount the challenges inherent in a relationship where someone is condescending because they have an implicit sense of personal superiority. And we all experience insecurity in ways that drives us to inhabit these spaces of superiority more often than we’d like to acknowledge.

So I’d like you to know about this worry I have.

But, there’s another piece in here that’s worth us considering. In the face of these odd diagnostic framings, I always want to ask: don’t we all have problems with social communication? Isn’t this a key part of being a living creature? Doesn’t every creature experience conflict as it occurs in any healthy relationship? There are whole fields of study, like philosophical hermeneutics, post-humanism and critical animal studies, which seek to confront the fascinating aspects of building understanding and the causes of misunderstanding in communication.

So rather than try to pretend you don’t notice when I’ve clearly missed your point, or I’ve read your reaction to something as more severe than you intended it to be, why not lean in to the awareness that you have trouble communicating sometimes too, that when you’re feeling tired and badgered by the world you might not have extra bandwidth for interpreting cues, mediating confusion or faciliting the process of bridging misunderstanding?

I’m fascinated by the ways that we hold culturally encoded double-standards around communication. In many cases, facilitating understanding by a listener or reader is taken to be a hallmark of skilled communication. This is undoubtedly the case, as I’ve learned from a lifetime of cross-cultural communication and teaching, which is often about troubleshooting how effectively you’ve been understood and learning to anticipate and surmount barriers. But if we’re being honest here, I think it’s worth acknowledging that being well-understood can also be a feature of having a homogenous social life and inhabiting hierarchies. It’s much more likely that, for most of us, we think of ourselves as easily understood and understanding people simply because we don’t spend that much time outside our comfort zone, staying within close-knit circles of people who share our experience, cultural background, social class, and particular competencies. There are forms of deference which are built into relationships where we are expected to mask misunderstanding and protect fragile egos.

What I really want to see is how a person performs when they’re thrown into a situation where they’re expected to communicate with people you don’t share much with. You can see this at work when people travel outside their home country for the first time, take an unexpected career transition, or move to a new place. Suddenly a person realises that their communication competencies do not arise from skills, experience and training, that those capabilities are more fragile than they’d expected and that there’s some hard work ahead. Moreover, when we are thrown into that kind of situation, we’re confronted with the sides of ourselves that emerge when we’re under stress: you may be impatient, sharp, slow to react, etc. and this compounds the embarrasment and difficulties of surmounting misunderstanding.

Some of the best teachers I’ve worked with are people who have placed themselves in situations of language and cultural diversity and developed forms of grace and patience for themselves and others which are the gateway to understanding. Some of the most skilled and empathetic communicators I know are neurodivergent people. Imagine how it might transform our organisations and families if we were more honest about how we’ve experienced breakdowns in communication, and more forensic about the aspects of our culture which drive us to conceal or hurry past misunderstanding in favour of quick and decisive action.

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As a long-time immigrant to a few countries, one thing we’ve gotten fairly proficient at is moving house in complex ways: securing visas, houses on another continent, banking with no credit history, etc etc. One of the most complex, and easily mistaken parts of the process is moving your goods overseas, and the stakes are quite high. Shipping by air can easily run into the thousands of £££ for just a few large items. As the freightos blog suggests, shipping less than 45kg by air can cost at least $34/kg whereas getting beyond 300kg gets all the way down to $5/kg, and if you use ocean freight this gets much lower. The current rate to ship an entire euro-pallet (800mm x 1200mm at 1m high) which will fit 24 small u-haul moving boxes from USSEA (the freight terminal in Seattle) to GBLGP (London) is around $250-300 so around $12.50/box which is around the same price as a single 45kg box sent by air. To ship an [ENTIRE 20 foot container](https://www.icontainers.com/help/20-foot-container/) runs around $3200 right now (as of Jan 2024). That 20′ container measures 6.10m long x 2.44m wide x 2.59m high, so it will hold a bit more than 960 of those small boxes, running you around $3.30 a box. The key difference here is the timing. Ocean freight can tend to run about 4-6 weeks to send items, whereas air frieght can be a week or less. But what are you in such a hurry for, really?

In this brief post, I’m going to provide a bit of a rundown on how things work and how to get the best deal for shipping your belongings. This can be achieved, if you are patient, by sea freight, that is, loading your boxes into a shipping container which is shipped by sea.

The first thing to observe is that when you’re working at pallet / container level, the size isn’t quite so high stakes. You could easily pay the same to ship 5-6 boxes by plane that you might spend to ship an entire 1m3 pallet of boxes as freight. So while I’m not going to encourage you to send your car or large furniture, it’s worth pausing giving away all your worldly goods. However, it’s also worth wondering how much you really care about belongings? Heirloom furniture, dishes, and Christmas ornaments all might have a particular sentimental value, and I can understand that completely. But what about your everyday dishes? Mattresses, couches and furniture? It’s likely you can sell or donate these things locally before moving and then when you arrive in your new home find replacements in charity shops or via online platforms like freecycle or freegle. This has worked well for us – and again, with a bit of patience and time you can furnish a whole house for very little expense, provided you don’t mind a bit of vintage and the charm of mismatched housewares.

So let’s say you’ve gotten down to your core belongings that are precious, unique, or useful enough to justify shipping (for me this involves pretty much all clothing which is rather hard to find in appropriate sizes). And let’s assume this is something like 20-30 boxes which will fit into 1-2 cubic meters of palletised shipping.

The next thing you need to appreciate is the process of logistics. Shipping involves a huge number of steps, which each need to be managed and insured separately. I’d estimate that at least half of the cost of freight will go to middlemen, er, logistics managers and freight forwarders, who coordinate across the many steps of gathering boxes from your home, taking them to the local logistics center, getting these pallets on a truck to the local seaport, arranging their safe passage on a boat, unloading at the sea port on the other side etc etc. Each of these steps is likely to be with a completely independent company, or at least a well-compartmentalised subsidiary within a large corporation. In any case, you need to be sure that every step will be tracked, accountable, insured, and documented. If your head feels ready to explode, then you should keep things simple. If you have the ability to drive your goods in a borrowed van to the seaport, then you may be able to skip a few steps and save yourself a lot of money.

It’s worth noting that seaports are not the same as airports, and sometimes the major seaports are hundreds of miles away from what you may think is a major airport. There are also major and minor seaports, and in most cases best to go for shipping to a major seaport and finishing the last leg with freight via truck, van, or your own car. You can look up UK seaports here: https://uk-ports.org/uk-ports-map/.

The key thing you need to get wise about here is “Incoterms”. According to freightOS: “Freight incoterms (International Commercial Terms) are the standard terms used in sales contracts for importing and exporting”. There are 11 different kinds of incoterms, which range from the bare bones service of FOB “Free On Board” where the shipper will literally deposit the items on a container ship (not recommended) to the nearly fullly covered EXW “Ex Works” where someone will pick up your goods at your premise and cover loading and insurance the whole way through. My advice is to try and reduce the number of hops in the journey, e.g. bring to the seaport, rather than try to work with lower tier incoterms. What you don’t want is to find out that you were supposed to show up in a distant city to unload something or that your goods were dropped on the curb uninsured somewhere you didn’t expect.

The next key logistic relates to the amount of cargo you’re shipping. For Ocean Freight there are basically two different sizes: LCL and FCL. LCL stands for “less than container load” and FCL is “full container load”. Essentially, the key thing here is whether you’re going to fill an entire shipping container, which run at 20 feet or 40 feet long. In some cases, if you’re past filling half a shipping container, you may as well just go FCL which is ususally billed at a flat rate, as the cost differeces aren’t going to be substantial with an LCL load charged per kg and cubic meter as I’ve highlighted on the first page above.

My advice is to figure out how many boxes you want to ship, round up by 20% and then start to collect quotes from shippers. And, again, I’d recommend you go with a provider who will handle insurance and customs costs bundled with the freight. I’ve found the tools at FreightOS to be pretty useful in getting estimates. FreightFinders will also give you access to a variety of shippers. Good luck with your move!

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Over the past two years, there have been some significant challenges that educators in Universities have had to confront, in many cases driven by top-down policy initiatives. There are a few different places where impacts have been seen – but one area I’d like to highlight in this post lies in assessment. The stakes are already quite high for practitioners striving to engage in the craft of pedagogy, as I’ve recently posted, because quantifying student achievement in a homogenous way (e.g. grading) is bound to cause problems. When we try to signal achievement in meaningful ways whilst working with forms of feedback that reduce something complex, layered, and multi-modal like human communication, we are bound to experience tensions and shortcomings. This is HARD work. But let’s assume we’re stuck with grading systems and don’t have access to more creative and collaborative options. If we accept this as our lot, then we need to work with these systems to mitigate their failures, especially in inflexibility (feedback to students around assessed work will inevitably hit up against limits and edge conditions) and opacity (our communication back to students in feedback is just as complex as their communication to us in their essays!). So at the very least, I think, we need to include flexibility in our approach, because humans just aren’t all the same, and our experiences and styles of learning are often radically different. Difference, in my view is best engaged in a relational way, and by extension is best managed at a local level. But can we actually do this in practice? There are some external factors intervening which educators in Universities need to be aware of as we try to develop careful and responsible policy around marking.

The policy landscape has shifted in the background over the past three years in the UK in ways that many academics (including myself) won’t have noticed. I’d like to break this down at some length as it helps to explain why we’ve gotten where we are just now (many readers may experience an “ah, so that’s why….!” moment) and unpack some of the parameters around how I think the educational landscape is being changed around assessment.

Part 1: A brief history of OfS interventions around Assessment in Universities

Over the past decade in the UK, there have been massive shifts in the way that government relates to Universities: shifting from a fairly hands-off mode under the Higher Education Funding Council for England (HEFCE) which was finally disbanded in 2018 towards a much more interventionist model driven through two new units: the Office for Students (OfS) and United Kingdom Research and Innovation (UKRI). Over the past ten years, and leading up to the formation of the OfS, government bodies involved in oversight and regulation of Higher Educatio have taken a much more interventionist stance on a variety of fronts within education, and assessment has been a particular target. This is in many ways a resurgence of the “Quality Wars” which have been on and off in Britain for several decades. Returning to recent history, from 2018 until 2021, Universities were regulated under The UK Quality Code for Higher Education (often called simply “the Quality Code”). This was generated in partnership with academic bodies and well regarded on an international level as a set of principles for setting standards in academic practice at Universities.

The Higher Education and Research Act which was passed by Parliament in 2017 initiated a process of review which eventually led to the launch on 17 November 2020 of a “Consultation on regulating quality and standards in higher education“. This process underwrote the drafting by OfS of a set of new policies, intended to replace the Quality Code, which were disseminated to the public alongside a lot of unqualified (and I daresay irresponsible) commentary around confronting “low quality courses” (WONKHE summary). The new policies were shared and feedback was drawn in from 20 July 2021 to 27 September 2021 (PDF here). There were serious questions about the intention of this process from the start (cf. this piece for WONKHE by David Kernohan and Jim Dickinson). Why for example, describe a process as consultative without meaningful involvement from students (much less educators) in the process of policy design? After that consultation, OfS produced a (largely dismissive) response to feedback from those two groups (PDF here). The conditions in that code came into force on 1 May 2022, just over a year ago. If you want to get into the nitty-gritty of how that public dialogue has unfolded, I recommend you read the various waves of analysis by interpreters on WONKHE which I’ve highlighted above.

These policies revolve around five long-standing “key conditions”: titled “B1”-“B5”, which are the following:

Condition B1: The provider must deliver well-designed courses that provide a high quality academic experience for all students and enable a student’s achievement to be reliably assessed.
Condition B2: The provider must provide all students, from admission through to completion, with the support that they need to succeed in and benefit from higher education.
Condition B3: The provider must deliver successful outcomes for all of its students, which are recognised and valued by employers and/or enable further study.
Condition B4: The provider must ensure that qualifications awarded to students hold their value
at the point of qualification and over time, in line with sector recognised standards.
Condition B5: The provider must deliver courses that meet the academic standards as they are described in the Framework for Higher Education Qualification (FHEQ) at Level 4 or higher.

These are seem to me like fairly reasonable things to aspire to in providing higher education. And while one might object to the back and forth of the policy process and the underlying instability this produces for students and academic staff in the sector alike, the actual headline suggestions seem quite unobjectionable.

However OfS process has pressed far beyond headline guidance and unpacked these in what seem to be forensic yet problematic ways in subsequent communication. The consultation documents acknowledge this agenda: “The main change from the current conditions is that the proposals include more detail about the matters that would fall within the scope of each condition and how these would be interpreted”. So the process didn’t just reshape the principles, but also indicated a shift from broad principles and quality monitoring towards much more explicit and specific policy delineation. This dialectic between specific rules and broad principles is a perennial point of debate between ethicists (like myself), and as you’ll find in my publications, I tend to prefer a principled approach, not least because it allows for a more relational and flexible approach to policy which can accommodate diversity and pluralistic communities (as I’ve highlighted above). Rules are better at controlling people, but they also run the risk of causing oppression and undermining the relationships in which education functions.

Back to the discussion at hand. It’s worth emphasising that the OfS is not playing around here: the potential consequences for a University which is found to be in breach of any of these conditions might be a refusal by OfS to validate their degrees and basically cancel graduation for that institution. Also fines. Expensive scary fines.

So what’s in the details? There are little landmines throughout the details here, as one might expect. I’d like to focus on the development of criteria for how “a student’s achievement to be reliably assessed” (condition B4). In the 2021.24 document, OfS provides further detail of the requirements entailed by B4:

B4.2 Without prejudice to the scope of B4.1, the provider must ensure that:

a. students are assessed effectively;
b. Each assessment is valid and reliable;
c. academic regulations are designed to ensure that relevant awards are credible; and
d. relevant awards granted to students are credible at the point of being granted and when compared to those granted previously.

They explain B.4.2.a in more detail a bit further on:

c. “assessed effectively” means assessed in a challenging and appropriately comprehensive way, by reference to the subject matter of the higher education course, and includes but is not limited to:

i. providing stretch and rigour consistent with the level of the course;
ii. testing relevant skills;
iii. requiring technical proficiency in the use of the English language; and
iv. assessments being designed in a way that minimises the opportunities for academic misconduct and facilitates the detection of such misconduct where it does occur.

In case this wasn’t clear enough, OfS provides even further guidance down the page, and colleagues may begin to notice here some of the drivers of policy which has been depoyed over the past two years by cautious University VCs and management eager not to fall afoul in the ways that their universities manage education and risk scary consequences:

50. In relation to “assessed effectively”, the following is an illustrative non-exhaustive list of examples to demonstrate the approach the OfS may take to the interpretation of this condition… “Marking criteria for assessments that do not penalise a lack of proficiency in the use of written English in an assessment for which the OfS, employers and taxpayers, would reasonably expect such proficiency, would be likely to be of concern. Students are not penalised for poor technical proficiency in written English. For example, for assessments that would reasonably be expected to take the form of written work in English and for which the OfS, employers and taxpayers, would reasonably expect such proficiency, the provider’s assessment policy and practices do not penalise poor spelling, punctuation or grammar, such that students are awarded marks that do not reflect a reasonable view of their performance of these skills.”

As you can see, we’ve gone from broadly unoffensive headline principles to some quite odd particulars very quickly in the OfS process. There are a number of strange things here: why single out “technical proficiency in the use of the English language” at all given how such things are already intrinsic to most degree programmes, especially within the humanities? If there are problematic actors in HE, it seems much more sensible to confront that on a specific level rather than deploy a redundant policy. But also, the emphasis here is not on measuring student achievement, but on penalising a lack of proficiency. There’s no interest here of celebrating a surplus of profiency. It’s also uncomfortable to find language here, reaching beyond student experience towards “the OfS, employers and taxpayers”. Many readers will be able to imagine a thousand different ways this could have been written (or not at all), less agressively, more constructively, etc. But let’s set aside these uncomfortable quirks for a moment and ponder together what exactly this all might look like in practice.

Part 2: Why does it matter?

One might come to the end of this and say something like, “yes, I wish this was worded more carefully and sensitively, but there’s nothing wrong with this policy in practice. And why not have more specific versions of our policy, much less these ones? Don’t we want our students to be effective communicators?” There are two key problems here which are complex and deserve our attention, not least because the victims are already on the margins of HE:

Problem 1: ambiguity and overcompliance

From a policy perspective, this is all a bit confusing. On one hand, it has the aspect of really highly specified policy. But on the other hand, even after reading all the guidance, it all seems quite murky. This issue with highly-specified policy which is nonetheless unclear on implementation details, is a recurring problem in the sector. The same issues apply to immigration policy in higher education, with providers often stretching far beyond the “letter of the law” in reaction to ambiguity which is intrinsic to policy demands.

How does one assess English language proficiency (for the “taxpayers”, of course)? Well, there are basically two ways to interpret this guidance. The first option is to take it at face value and do the specific things they mention, e.g. include in our processes of assessment specific checks penalising people when they show they are not sufficiently proficient in spelling and grammar. As I’ve said above, these things are already widely practiced in the sector, so one couldn’t help but begin to wonder, is there somethign else I’m missing here? There’s an important phrase in the text above where the guidance explains what “assessed effectively” means. Did you see it? It’s the part which says “includes but is not limited to”. So is one right to assume that these very (even weirdly) specific examples are the policy? Or is this just a sort of initiation into a pattern of behaviour that educators are being pressed into? So here we have seemingly highly specified policy with loads more detail than previous policy guidance had in it, associated with really scary and dire consequences for people who breach these new policies, and some intimations that we need to develop regimes for surveillance to punish wrongdoers but even at the end it’s clear that “there’s more…” without specifying what exactly that means. In organisations where there are administrative staff whose job is to mitigate risk, when situations of ambiguity arise, the resulting policy response will veer to the side of caution, especially if there are fears of serious consequences looming if one is cast into the limelight for falling afoul of a policy. So in some cases, organisations do just what they’re told. But in many cases organisations reply to policy in extreme ways – this has been the case in response to prevent policy on campuses, immigration and right-to-work checks, even response to GDPR policy is implemented in ways that on closer look are unnecessarily extreme. So at the very least, when you’re faced with a situation like this, there is an increase in the hazard that the resulting implmentations or responses to a policy demand may reach much further than they need to.

Problem 2: linguistic competency

Things get even murkier when you start to really try and work out the details. Marking spelling accuracy is dead easy, and students ought to know better as they can easily access dictionaries and spell checks. We do penalise sloppy work in terms of misspelling, I know this is the case from years of practice as an educator, and also an an external examiner for a variety of Universities. The same is true of punctuation. The rules are highly standardised and easy to access. But what about grammar? On one hand, verb tense is pretty important to get right. And there are guides which can check this. From years of reading, I can spot bad grammar almost automatically on the page in a variety of ways. But what exactly does it look like to be maximally proficient in English language usage? What exactly are the rules we follow that make communication effective? This is where things get murky.

There are a number of examples where some people might point to the formality of prose as an important standard, my students every year ask me if they should use personal pronouns, “I think that…” versus “One might think that…”. And my answer is always, “it depends”. Formal language can be helpful, but sometimes it can be impersonal and inaccessible, even a bit stiff. Another example is the use of complex terms. Does proficiency in English language rest on the use of five syllable words? Or is the most compelling prose driven by neat sparse vocabulary? And even more pointedly, what about the use of slang, vernacular, pidgin, or creole terms and idioms? Are those kinds of language casual? Formal? Technical? I can think of many cases where scholars and poets I admire forced me to think through a particular vernacular or regional lens with their writing in English and this elevated their arguments. And as someone who has lived in a number of different English-speaking nations and regional cultures (USA, Canada, Scotland, England and Wales) I can attest to the ways that idiomatic English can vary wildly, both within elite and non-elite cultures.

Once we get past the basics of spelling and grammar, it gets really tricky to actually say what the rules are, and explain how and when they should be broken. Ultimately, good prose just feels right. We can judge its effectiveness through affect and intuition. But also, our judgement relies upon sympathy with the writer: I work hard to understand a poet’s use of vernacular, metaphor, etc. because I trust that they have something to teach me. Another reader could just as easily pick up the poem and conclude that it is obscure and poorly written. I don’t mean to suggest that there are no standards, but that they are not easily accessible and that teaching them is complex and requires a lot of recursive effort. And successful judgement like this only becomes accessible at the mature end of a learning journey and not checkpoints in the early stages. And can we really say that there is such a thing as “Standard English” (SE)? Even within my own department, and in specific taught modules, I can point to a variety of contextually meaningful differences around how to use language to communicate towards certain ends. And if we were to go for the lowest common denominator, is there any way we could say this is more than simply writing mechanics like proper spelling?

There are also ways that working with this kind of intuitive sensibility leads us to rely on personal familiarity – things which “feel right” as an implicit proxy alongside the more rigorous and obvious formal rubrics which establish conventional grammar and spelling. For all these reasons, the measurement of language competency is a hotly conteted topic among specialists in linguistics. At the very least, it is generally taken to be the case that measuring this effectively is extremely difficult, even for highly trained specialists and best not attempted by amateurs (like me, as I have a PhD but am not a scholar in linguistics).

So it’s hard. And potentially it’s pretty likely that any intelligent person, even University faculty, will make mistakes in assessing language competency – at the very least mistaking written communication that “feels familiar” as proficient. But why is this such a big deal?

The hazard here sharpens even further when we apprecaite how, in spite of our best efforts, the aesthetics of student feedback processes can collude with forms of racialised, class-encoded, ablist, and enculturated privilege. Linguistic racism and misogny is widely recognised as an issue in workplaces, impacting customer relationships, undermining team communication, and widening inequality in hiring processes. This has been investigated in some specific case studies into the ways that vernacular language can be stigmatised and used as the basis for discrimination and negatively impact academic outcomes. In particular, studies at Universities have found lower levels of achievement for Black students when compared to their counterparts who are racialised as white. This in turn has been linked, albeit only in part, to the stigmatisation of African American Vernacular English (AAVE) over so-called Standard English (SE). Seen in this way, forms of ethnic identity embedded in language patterns defeat the anti-bias intentions of anonymous marking and create a context for inequality in marking. A wide range of solutions have been proposed in pedagogical literature, including (particularly among proponents of student-centred learning) forms of teaching which encourage the reading and use of vernacular speech as a prelude to critical engagement with vernacular culture more broadly. Given the amazing levels of diversity that we enjoy in our Universities, it would be well worth engaging in a corporate discussion around how we engage and celebrate vernacular (if indeed we do), and conversely, how we can mitigate discomfort by other students and staff who are unaccustomed to deviations from Standard English (SE). However, setting aside this more proactive aspiration, it seems to me to mark a step in the opposite direction to introduce punitive measures for markers who find lacking English language proficiency, particularly without any specific guidance as to how vernacular language might be handled, and how evidence of research and understanding might be affirmed in ways which are separate from language flow. All human cognition mobilises bias, so it’s not enough to aspire to an unattainable “unbiased” state of mind, but rather essential to acknowledge and understand in an ongoing way how our biases, especially implicit ones, mobilise forms of privilege in our teaching and research.

In my teaching, I introduce students to the importance of local and culturally inflected forms of knowledge in responding to public policy challenges like climate change. We also discuss how cultural fluency and dynamism can serve as a transferrable skill, enabling our students to support workplaces which want to reach new audiences and develop forms of end-user engagement which are culturally relevant. In particular, within Theology and Religious Studies we discuss the importance of vernacular culture as tools for community development, we introduce students the ways that feminist scholars and poets deploy informal language, like contractions, and use creole and vernacular vocabulary as a way of challenging unjust hierarchies and emphasising the social good of diversity in practice. I even hope that my students may come to deploy those tools themselves in their written communication, thinking about the ways that forms of communication transmit not just information but personal and social values. I fear that education policy in Britain may prefer to pay lip service to the goods of diversity, whilst failing to provide support and infrastructure to underpin these kinds of learning.

Part 3: What should we do?

The OfS has created a really unfortunate challenge here, ultimately risking the recolonisation of education (contradicting our glossy brochures which advertise our de-colonising work). I gather from reading the OfS response to the policy consultation that there was quite a lot of unhappiness about the policy. This included, as the report indicates,

  • Suggestions that it was not appropriate for the OfS, as a principles-based regulator, to prescribe how a provider should assess a student’s language proficiency beyond the course learning outcomes, and whether it was possible ‘to infer what employers’ and taxpayers’ specific expectations might be in any particular circumstance.
  • Views that English language proficiency receives disproportionate attention in the proposed guidance, with respondents questioning whether it is a priority for non-UK partners or appropriate for all TNE courses.
  • Disabled students or students for whom English is a second language may be disproportionately affected by an approach that expect proficiency in the English language as a relevant skill. For example, one respondent commented that the ‘focus on proficiency in written English has potential implications for institutional approaches to inclusive assessment, which are designed to ensure that students with specific disabilities are not disadvantaged during assessments, and to thereby comply with the Equality Act’.
  • The level of English proficiency required would be better considered on a subject or course basis, based on academic judgement about whether mistakes in written English are material or not.

I’ll let you read their resposes (starting on page 25)

It has become clear to me that protest is not an option. There is simply too little will within the sector leaders to respond to OfS, at least overtly, with refusal to comply and a demand for more sensitive education policy (I would be delighted to be corrected if I’m wrong about this). So there are a two different models of compliance that seem viable, within some specific conditions, to me:

(1) Affirm ways that assessment criteria and feedback already achieve these demands. This is true of all the the teaching contexts where I’ve ever worked or assessed as an external examiner in the UK, so ultimately a viable option, though I accept that there may be some contexts where this isn’t the case. I don’t see any reason why these can’t be handled on a case-by-case basis.

(2) Make very specific additions to marking criteria. It may be that there are some cases where it becomes clear that spelling and grammar haven’t been part of assessing student work. If that’s the case, then the least harmful and hazardous way to achieve compliance here is to be highly specified: “spelling and grammar”. Reaching beyond this in any particular way will enhance the risk and dynamics of bias in assessment.

It’s worth noting that the latter option might be chosen not because there are clear lapses in educational design, but out of a desire to be seen as “doing something”. There may be a fear lurking here (which I’ve alluded to above) driven by the dynamics of over-complicance, that we need to do something visibe in response to the “new” demand to avoid surveillance and punishment. I’ll note here my reservations as an ethicist with any engagements with policy that are arbitrary. There will always be problematic and unfoeseen implications lurking downstream as our use of arbitary policy begins to accumulate and iterate. And these consequences often tend to impact persons (both educators and students) who are already marginalised in other ways.

My anxieties here around bias in marking align with some other work I’m running in parallel with some stellar colleagues around stereotype threat and implicit bias. I’ll be writing more in months ahead about these projects and what I think we can do to proactively address the harmful impacts these phenomena have on students and teachers more broadly in our teaching practice. I’m also working with colleagues to find ways to more proactively celebrate linguistic diversity in our teaching in higher education and will share more about this work as it unfolds. But it’s worth stressing at this point that it is irresponsible to implement harmful and risky policy (even in the current atmosphere) with the expectation that we (or others) will mitigate those problems after the fact with bias training. This is highlighted well in a recent piece by Jeffrey To in Aeon.

I’d be very glad to hear what colleagues have attempted and how educators are responding to these challenges across the sector and hope you’ll share from your experience in the comments.

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Image of an hourglass half buried in the ground

In recent years, the British government has been pushing Universities to implement workload allocation models tracking staff time. This is at least notionally, about providing transparency and accountability around public spending around higher education. I fear, however, that it is more about promulgating a disengenuous model of “lazy academics” sitting around using government money and the need to control us more carefully. The origins and impact of this narrative as well as some helpful refutation from actual realiity are covered extensively in Peter Fleming’s Dark Academia (blog post book review from LSE linked here). But that is the reality that we’re under. And, truth be told, many academics have embraced these systems with open arms in hopes that they will proivide a utilitiarian tool for reducing their overwork and inequalities within the sector around workload. My observation so far is that they have increased and worsened these problems and privatised suffering by concealing it behind impersonal systems which can’t be confronted or held accountable. I’ll accept that there are likely exceptions to this and would be glad to hear if anyone has experienced systemic improvements in justice within their academic workplace as a result.

But this has led to a shift in the model by which academic workload is measured – from forms of work to time units. It used to be the case that we’d talk about sitting on a certain number of committees, teaching a certain number of modules, etc. but now all of these are converted into specific homogenised time units (“WAM Points”). I’ve worked in other sectors where workload is managed in this way so it’s nothing new to me, but I had thought for a moment that I’d escaped it, so have found this resurgence personally discouraging.

I’ve been thinking about this lately, in particular as I work in increasingly overt collaborations with other neurodivergent colleagues, and I’ve observed that this shift in workload management, surveillance and sanction has hit ND staff particularly hard. Given my research over the past decade has focussed on the philosophy and theology of time, I’ve hit upon some speculative conclusions I’d like to test out about time experience and this policy shift. In particular, I wonder whether neurodivergent people experience time in more variable and intense ways than non-ND.

Post-Taylorist scholars in business and organisational studies have begun to observe that time is not homogenous. And in really obvious ways our embodied experience of normal tasks is certainly not this way. Think of how you can sit a read a novel and the hours pass unnoticed, where in contrast you might find when completing an onerous task that the time passes with aching slowness. This is also the case with joyous work, however, as the bodily impacts of exercise are quite different from relaxation. Our hour of deeply pleasurable sprinting is still accounted differently in our bodies from an hour of walking. So too it must be at work: different kinds of activities have different levels of physiological demand on us. In some (limited) cases, workplace studies scholars (and even managers!) have built “recovery time” into specific kinds of tasks on the basis of this awareness. But it’s not just the tasks themselves, but also the “between times” and in other cases, scholars of work have noticed that “idle time” is a common and necessary feature of work providing padding around difficult tasks and opportunities for creative and non-linear thinking around problems. So too workplaces, especially in tech have emphasised unstructured time as part of a normal working week. It’s important to emphasise that for academics, at least in my experience, the block allocation we get for research time is NOT this kind of thing, as we spend most of the year being pressed for demands around production and that time is probably the most pressured of any I experience. Have a look at the ways that sabbaticals are handled now – we’re expected to write an extensive application detailing all the specific tasks we will complete and achievements we will attain, and are pressed relentlessly to report on this when that time has concluded to confirm that we have completed the list we have offered.

All of this things are true for any person who occupies a human body. But I think these things are far more intense for autistic people where flow and pace are far more intrinsic to executive function, working at tasks in a kind of self-generated sequence can be essential. I mention this a bit in a previous blog post where I talk about a “day in the life“. The tragic thing about this is that when they aren’t subject to trauma, coercion or control, when engaging their passions (like pretty much every academic I’ve ever met) autistic people will pursue tasks with unusual tenacity. So trying to account for our work in a mechanistic way is oppressive, but also unnecessary as we’re likely putting in long and strange hours to complete our work above and beyond, simply for “love of the game”.

This has some really concrete ramifications for workload management, however, as it foregrounds the ways that individual tasks can have quite different demands on people, especially in the case of neurodivergence. And these models deliberately disallow inflecting time burdens in different ways for different people. The expectation is usually that some things will be hard and others will be easier, but this really mobilises the ablist “superpower” narrative in unhelpful ways, e.g. if you are slowed down in one area, you must have a superpower to compensate for another area so you can “keep up”.

In a similar way, having recovery and buffer time is even more necessary, as we adapt to group work patterns which are unadapted and hostile. It was once the case that I could mask and conceal my own disabilities by offloading tasks that took me far longer, or were demanded in moments when I didn’t have energy or ability to complete them, into spare time. But increasingly our models exclude spare time as a general rule, require work on short notice and rapid deadlines, and I’ve found that there’s simply no place to put those things temporally.

The key point here is that if we can talk about and negotiate our shared workload together around tasks and abilities, things are quite different. But when we work with impersonal homogenised time, the guaranteed result will be oppressive for specific (perhaps all) people.

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In recent years, one of the joys of my work at the University has been to convene a regular tutorial / support group for neurodivergent students. As part of the process of unmasking and reflection, I’ve come to confront so much about my own past learning in University which was a (lonely and terrifying) struggle, from sensory overload, challenges processing and hearing lectures, processing information in different ways, and navigating frequent meltdowns and overload. Chatting with students (who have far more self-awareness at this stage in their University journey, but are still trying to navigate these challenges!) has been really meaningful. Our discussions have also been quite interesting from a research perspective, as we delve into points of pedagogical friction and dysfunction which are a regular part of their experience and to try and troubleshoot how we might adjust, confront, or repair those areas of exclusion where our curriculum doesn’t always map onto the diversity of our learners. We have found some things which are (or might be if we could find suitable allies around educational policy, which is sometimes a quest unto itself) easily fixed with small hacks, in other cases, it’s really just a matter of being able to speak aloud about challenges even when there’s nothing to be done. But then there are some issues where we identify a challenge which is much harder to pin down. It’s often the sort of thing where you might be tempted to dismiss it out of hand, but when 7 or 8 people all seem to have the same experiences, that cooroboration helps to identify something that requires further investigation.

I’ve been chasing one of these for a couple years now which runs something like this: some of our students experience really “spiky” performance in marks they receive for assessments. When I say spiky, I mean in the same semester they might get one of the highest marks we’ve ever awarded for a particular class, and at the same time they might be just above a failing mark in another. In some cases this happens because, when you’re navigating a learning environment which is constantly stressful and traumatic, energy levels can suddenly drop and executive function can evaporate. But even if we bracket out instances where this has been the case, there are other situations where learners submit a raft of essays, all composed with the same level of energy and deposited with the same level of confidence and the results are highly idiosyncratic and unexpected. This was very much the case for me as a learner: I managed to push my way through (obviously) higher education, but it was always by the skin of my teeth, fretting about that one module or essay that I’d nearly failed whilst getting superlative results in others. Let me emphasise, the phenomenon that I’m highlighting here isn’t a matter of ability suddenly flagging, or finding an area where I was lacking understanding or expertise. Sometimes I’d hand in an essay where I felt like I was saying something really important and meaningful, and the marker would return it with feedback indicating that they clearly didn’t understand what I was trying to do. This was (and is) usually framed as a failure to achieve proficiency in written communication. Because it’s always our fault when someone can’t understand us, right?

Since then I’ve learned about the double-empathy problem, originally developed by Damien Milton (original paper here). Researchers into autusim have consistently pursued this hypothetical frame – that breakdown in communication and understanding must lie within some pathology of the autistic person. This has been framed around “theory of mind” – the condescending, theoretically and empirically problematic suggestion that autistic people lack the ability to empathise or understand the mental states of other people, also sometimes called “mind blindness”. This much more comprehensive pathologisation of autistic lives can be confused with alexithymia (something I experience, as I relate here: A day in the life of neurodivergence) which is a much more specific condition, and which has been tied to both hyposensitivity (getting too little information from reading other people) and hypersensitivity (getting an overwhelming flood of information about the states of other people from microexpressions and body cues, which can be hard to parse when you’re under stress). It’s also the case that there is a very high co-incidence of trauma, at the levels of CPTSD for autistic people, which has effects (which can be addressed therapeautically) in impairing our ability to read other people (e.g. through the constant triggering of our threat perception and response). (some) Researchers have begun to be much more cautious about engaging with older theories around theory of mind, especially after they have begun to take into account trauma-informed approaches to experimental psychology. Getting back to Milton’s work, in setting aside the tendency to pathologise autistic people, Damien hypothesised that this lack of understanding, when it occurs, might happen to a much wider range of people. That breakdown in communication might arise from forms of cognitive difference, and seen in this way, might occur in BOTH directions. Milton wondered if it might be possible that autistic people might experience less communication breakdown when communicating with other autists and conversely if it might be possible to set up experiments which verified this was occurring. This research is just starting to coalesce as a field of study, but my reading of the literature (for an example, see Muskett et al 2009) is that this hypothesis has been confirmed and this requires substantial revision to psychological theories of autism.

The reason I bring up double-empathy is to ask, whether in the course of teaching and learning, we may have two-way breakdowns in communication, where written communication is the goal of the learning process. Is it possible that faculty (both autistic and allistic) are conveying prompts inviting students to write an essay which can be misunderstood when bridging neurological difference? And similarly, is it possible that students are writing essays which might be received quite differently, and even marked quite differently, when read by staff who are autistic or allistic? To be clear, as I’ve related elsewhere in blogposts, I think that the much heavier burden here is on allistic staff as autistic staff will have had a lifetime of training (sometimes on the level of conversion therapy) in interpreting and understanding communication across neurological difference. The especial challenge here is whether the opposite is true. I fear that in some cases it is not.

For now the advice that I give to students arises from my own experience: your learning process around written communication, especially where it will be largely evaluated by standards which exclude the salience of neurodivergence, will be spiky. You will be misunderstood, and there are few pathways to open up converastion with lecturers about this experience in practice unless you are willing to pathologise yourself (e.g. “I was under great stress and my writing suffered”). There are no mechanisms for faculty to assess their relative incompetence in understanding different forms of communication. And I see policy directions in higher education which are driven towards increasingly homogenous and binary assessments of written communication (good English v. bad English) which has implications for a wide variety of student and not just neurodivergent ones. I tell students that their learning journey is going to have a longer arc than they expect. I found that my own writing didn’t “click” with audiences consistently until I had time to synthesise the many many horizons I was trying to integrate (and this sense that one needs to integrate everything is a common experience for autistic learners). It wasn’t possible for me to compartmentalise in the ways that many other writers and learners do, which ensures a level of success in their early stages of education. It’s likely that I’ve also found an audience which has been developed over time, with readers who understand my broader project, have sympathy for and interest in it, and are able to “jump in” and understand what I’m trying to achieve.

The question I’m holding for right now is whether there are ways we can adjust our processes of teaching to adapt to a wider range of written communication styles, and celebrate the fact that learning journeys are often quite different. It’s possible that we cannot achieve this kind of adaptation without some radical reconfigurations. I’ve tried much of the fine-tuning approaches already in my own practice and with collegaues, and have not found much in the way of effects. I think that calls to abolish grades are probably a key part of the discussion we need to have around how we can more effectively configure the coaching relationship with student writers. The core issue here relates to neurodiversity on campuses – aside from box ticking and PR exercises, how far are we willing to go to craft pedagogy which embraces diversity and doesn’t punish it?