One of the ways my neurodiversity manifests itself is that I think in systems. I tend to see relationships very quickly: if A is connected to B, I can also see B’s relationship to C, the institutional conditions that make C possible, the implications for D, and the exception that complicates all of them. This is extremely useful for research. It is not always useful for writing a journal article.
The problem is not that those connections are wrong. It is that a paper cannot give equal weight to everything I can see. I have found AI incredibly helpful for introducing structure into a moving constellation of different thoughts, but perhaps more importantly, I have found it useful for disciplining my writing. I do this through what I initially called a voice prompt, although I now think of it more as an academic voice and writing prompt.
A voice prompt is more than stylistic mimicry
A poor use of AI would be to upload a paper and ask: “Can you improve this?” That tends to produce the bland AI voice that is increasingly easy to recognise: grammatically competent, smoothly structured, full of apparently sensible transitions, but often lacking intellectual depth and, quite frankly, a bit boring.
A better use would be to give AI examples of your writing and ask it to write in your style. That can help it recognise some of the visible features of your voice: tone, cadence, sentence structure and preferred vocabulary. If you provide enough material, it can also begin to identify what matters to you intellectually.
For example, my own prompt tells AI to bridge macro-structural processes with everyday lived experience; foreground geography, infrastructure, relationality and the conditions through which action becomes possible; avoid individualising or behaviouralist explanations; and use relational verbs such as shapes, mediates, enables, constrains, organises and reconfigures. It also tells it to combine empirical specificity with conceptual framing. These are not simply stylistic preferences. They describe something about how I understand the social world and what I think geographical scholarship should do.
But the method I am proposing goes further. It is something more like this: here is how I tend to think and write; here are the intellectual qualities I want to preserve; here are the mistakes I repeatedly make; here are the tests a strong paper must pass; challenge me when I violate them.
That turns AI from a mimicry device into a reflexive scholarly tool. Importantly, AI is not deciding what good scholarship is. You are.
From voice to scholarly discipline
The rules come from experience: papers that worked, papers whose contribution people failed to grasp, reviewer responses, journal rejections, things colleagues repeatedly asked you to clarify, and eventually recognition of your own habits. Some of the most useful instructions in my current prompt are not descriptions of my voice at all. They are restraints on it.
It tells me not to attempt to solve the whole field in one paper, but to prioritise one dominant intellectual move, one central mechanism or process, and one memorable contribution. If several concepts are present, it tells me to identify which one carries the contribution and give every other concept a specific supporting job.
A good example from my own writing is my 2019 paper More than Just Food. When I went back to it recently and asked AI to identify its contributions without trying to decide which was the main one, the resulting list was rather revealing.
The paper argues that food insecurity does more than produce hunger and poor health: it erodes social networks, community spaces, local foodscapes, skills and other resources that communities need in order to act together. It argues that self-organisation is not simply voluntary or leisure activity but a vital community capacity. It shows that different forms of food support can either merely respond to need or help rebuild some of those depleted capacities. It argues that resilience cannot be produced locally while larger-scale policies continue to undermine the resources on which it depends, and therefore requires a multi-scalar response.
Running through all of this is a particularly important recursive argument: the same neoliberal conditions that increase communities’ vulnerability and make collective action more necessary also erode the resources that make collective action possible. All of those arguments really are in the paper.
The problem becomes obvious when I look at that list now. Which one is the contribution?
The paper itself does not make the reader choose because I did not choose firmly enough either. The abstract actually announces its findings as “twofold”: first, that food insecurity extends into places by eroding community resources, and second, that the form food support takes affects communities’ ability to rebuild those resources. It then adds that self-organising is a vital community asset and concludes with the need for multi-scalar policy. The conclusion expands again, moving through the hollowing out of places, social infrastructure, community assets, self-organisation, food interventions, individualisation, resilience and multi-scalar policy.
Nothing there is particularly extraneous when you can see the system connecting it all. But the intellectual labour required to work out what should organise everything else is left too much to the reader.
This is not a bad paper. It plainly is not. There are several substantive contributions in it, and the recursive relationship is already present right at the beginning. The paper describes community organisations trying to “buttress and rebuild—rather than simply backfill” resources that neoliberalism is simultaneously undermining. The problem is contribution signalling and hierarchy, not absence of intellectual content.
The consequence is that an important contribution can be overlooked, or readers may be unsure what they should take from the paper and carry into another debate. That limits how far the paper’s strongest contribution can travel, despite the originality and significance of the underlying argument.
If I applied my current writing prompt to that paper, it would force me to establish a hierarchy. I think it would tell me that the strongest move is not simply that food insecurity is “more than just food”, nor even that community organisations can build resilience. It is that collective capacity is itself socially and materially produced: the conditions that create the need for communities to become resilient can simultaneously deplete their capacity to organise a response.
The erosion of foodscapes, social networks, skills and community spaces then becomes the mechanism through which that happens; the different forms of food support become evidence that those conditions can be rebuilt differently; and the multi-scalar policy argument becomes the consequence. The same material is still there, but now it has a hierarchy.
Those instructions are there precisely because systems thinking makes me inclined to see lots of things that are genuinely connected and then try to retain too many of them in the paper. The aim is not to make me think less systemically. It is to prevent every part of the system from demanding equal space on the page.
Building your own academic voice and writing prompt
Building this kind of prompt is easier if you have a substantial body of writing to draw upon, but that does not mean early-career scholars cannot do it. Your own writing should remain fundamental because the object is not to manufacture somebody else’s academic persona.
Write in order to establish a voice. You can then augment this with papers you admire. I do not necessarily mean papers closest to your subject, or those that appear most theoretically elaborate. Theoretical sophistication does not require dense prose, heavy jargon or unnecessary complexity. In fact, some of the strongest conceptual writing is powerful precisely because it makes difficult ideas easier to see rather than harder to access.
I would choose papers where the writing feels both accomplished and clear: papers where you can understand the intellectual problem, see why the proposed concept or framing is necessary, follow the reasoning that gets you there, and recognise quickly what becomes possible once you adopt it. The point is not to imitate someone else’s style, but to identify the qualities of writing that allow ambitious ideas to travel.
If you are not sure why your own papers keep being rejected, are described as incremental rather than field-shaping, or simply seem not to land as strongly as you think they should, your existing work can become evidence. Give AI a group of papers where you think the contribution has not fully landed and ask what you repeatedly do well, what intellectual moves recur, where your arguments become difficult to follow, what you repeatedly overdo, where several concepts compete for attention, whether you tend to describe a mechanism without naming it, whether your introductions promise the same paper that your conclusions eventually deliver, and what AI must actively resist doing when working with you.
Reviewer comments can help too, at least the useful ones. You can ask AI to look across them for recurring criticisms: over-explaining, repetition, inconsistent terminology, too many theoretical actors with no clear hierarchy, insufficient explanation of concepts, weak signalling of the contribution, descriptive empirical sections, conclusions that introduce new arguments, or a failure to explain why the paper belongs in the journal to which it has been submitted.
The point is not to treat reviewers as infallible. They plainly are not. It is to look for patterns.
The four-question test
My current prompt contains four questions that I ask AI to keep central whenever we review my writing. These are designed to test whether I am clearly presenting the argument:
- What does the existing literature currently make difficult to see?
- What is the single intellectual move the paper makes?
- What mechanism or process does it identify that explains something we could not adequately explain before?
- What becomes thinkable or actionable differently once we see this?
The questions are intended to form a chain: blind spot → intellectual move → mechanism → consequence.
I like mechanisms, so that chain reflects the kind of scholarship I am trying to produce. Yours might be different. A methodological paper, historical paper or descriptive ethnography may require a different sequence. The point is to make the sequence explicit.
For me, these questions are also a way of preventing a familiar and expensive problem: writing 9,000 words, showing them to somebody, discovering that they cannot quite tell what the contribution is, radically rewriting the paper, submitting it, getting rejected, and beginning again.
AI cannot guarantee that a paper will be excellent or accepted. But it can help expose some of those problems much earlier.
What the prompt would have changed: Buying Local Food
My 2010 paper Buying Local Food: Shopping Practices, Place, and Consumption Networks in Defining Food as “Local” is another useful example because there is a good intellectual move in it. It remains one of my most cited papers.
The paper argues that much local-food research at the time looked down the commodity chain from production toward consumers. We reversed that perspective and looked “up” the chain from ordinary food provisioning. That made visible something conventional accounts of local food tended to obscure: local was not a simple property of a food item, nor simply a question of distance from farm to fork. It was being produced relationally through consumer practices, retailer strategies, place, price, convenience, quality, class and racialised meanings.
A key observation in the paper is that shoppers are effectively practising geographers. They know which foods are available where, at what times, at what quality, how far it is worth travelling, and how time, distance and price interact.
Seen through my present-day writing prompt, the paper has a reasonably strong chain. The blind spot is that local-food scholarship made it difficult to see how “local” was produced through ordinary consumer-retailer relations in place. The intellectual move is to reverse the analytical gaze and start with provisioning practices rather than the producer. The mechanism lies in the interaction between retailer strategies, consumer practices and geographically situated possibilities, which together produce both meanings of local and the shopping environments within which subsequent choices occur. The consequence is that “buy local” cannot sensibly be treated simply as an informational problem or an individual consumer choice.
Seen retrospectively, this is almost a textbook example of my systems-thinking problem. Once the main move is established, I continue following every consequential connection I can see.
The conclusion talks about food miles, meanings of local, convenience, health, status, class, race, farmer supply chains, imported foods, gardening, education, planning, retailer responsibility, food quality, cultural capital and, finally, a broader theory of place. None of these connections is absurd. Several are quite interesting.
But if I were writing the paper now, my prompt would probably tell me that I have already made the intellectual move, and that I need to stop adding other contributions. It would ask which of those insights demonstrates or extends the central argument and which should be subordinated or removed.
The paper itself eventually reaches a powerful implication: simple labelling and “buy local” initiatives place responsibility on consumers while leaving retailers and the wider organisation of food provisioning comparatively untouched. I would now bring that much closer to the centre of the paper.
Using the prompt as a reviewer
When I am preparing a paper for a particular journal, I now add another instruction: why is this paper for this journal rather than simply a good paper that could appear somewhere else?
I tell the AI the target journal and ask it to examine the kinds of intellectual interventions that journal publishes. I also ask it to check whether I am engaging meaningfully with relevant recent work from the journal. The important word there is meaningfully. Dropping three citations from the target journal into an introduction is not journal fit. Those papers should actually help establish the problem, theoretical conversation or intervention.
The test in my current prompt is essentially this: could an editor read the abstract and introduction and explain why readers of this particular journal need the paper? If not, the problem may be framing, positioning, conceptual ambition, or simply that I have chosen the wrong journal.
If you get an academic voice and writing prompt right, AI can begin to function like a reviewer or writing companion. It can tell you that something needs elaboration. It can notice that you have quietly changed terminology halfway through the paper. It can point out that a fascinating three-page discussion has very little to do with your stated contribution. It can tell you that the mechanism you think is obvious has never actually been stated.
The prompt is also portable. You can load it into different AI systems alongside the same paper and ask each of them to review the manuscript. In my experience, they do not always identify the same weaknesses, which is useful in itself.
But none of this removes the need for scholarly judgement. Read what it produces. Does the criticism make sense? Has it misunderstood your argument? Is it asking you to make the paper more conventional when the point of the paper is precisely to challenge that convention? Would accepting its advice actually weaken something distinctive about the scholarship?
Engage with AI much as you would engage with reviewers. You do not have to accept every criticism. But if you reject one, you should ideally understand why.
The purpose of an AI voice and writing prompt is not to eliminate your peculiarities. A good prompt should not normalise the scholar. My systems thinking is not a defect I want AI to correct. It is part of how I notice relationships, mechanisms and possibilities that might otherwise remain difficult to see. What I want help with is deciding which part of that system this particular paper is going to explain.
A useful prompt therefore creates conditions in which distinctive ways of thinking can travel more effectively to readers. You cannot enter an academic conversation effectively if nobody can understand what you are trying to say, however interesting the argument may be at its core.
A good academic voice prompt does not just tell AI how I write. It tells AI what should also be resisted.
GeoFoodie 