Category Archives: Research

These posts and pages are drawn from research presentations and publications that I have produced.

Using AI to improve your papers: a voice prompt is not just about voice

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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:

  1. What does the existing literature currently make difficult to see?
  2. What is the single intellectual move the paper makes?
  3. What mechanism or process does it identify that explains something we could not adequately explain before?
  4. 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.

AI Is Not Your Author: How I think with AI

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People often ask me how I use AI in my research.

This is my answer.

There is a great deal of advice at the moment telling students and researchers either not to use AI at all or to embrace it enthusiastically. Much of the public discussion has focused on plagiarism, authorship, academic integrity and whether AI should be used in universities at all. These are important questions, and they deserve serious attention. Over the past year, however, I have become interested in another question that seems to receive much less attention:

How might AI change the conditions under which we think?

I do not mean that AI does the thinking for us. I mean that it may change how some of us organise ideas, remain engaged with difficult problems and recognise patterns that we have struggled to articulate.

I first became interested in AI because I am neurodivergent. I often know what I want to say but struggle to find the words, identify the structure or feel confident that I am making sense. Before AI, I sometimes avoided writing not because I lacked ideas, but because I could not see how to begin organising them. The longer I left the task, the larger and more difficult it became.

AI did not solve that problem, but it changed how I encountered it. Instead of staring at a blank page and trying to settle everything internally before beginning, I could start with a conversation.

This distinction matters because scholarship begins long before words appear on a page. It starts with curiosity and involves reading, noticing, connecting ideas, questioning assumptions and gradually developing judgement about what matters. Writing is one way that scholarship becomes visible, but it is not scholarship itself.

I enjoy research. I enjoy finding patterns, developing explanations and working out why something happens in the way it does. I also enjoy explaining ideas to other people. What I do not enjoy is the struggle that can arise between understanding something and finding a form in which I can communicate it. That struggle can absorb energy that I would rather devote to the ideas themselves.

AI helps reduce that friction. It gives me a space in which I can think through writing: testing an idea, reorganising it, objecting to an interpretation and trying again. It does not remove the intellectual difficulty of scholarship, nor would I want it to. What it changes is my capacity to remain connected to the part of the work that gives me pleasure.

One unexpected consequence is that I now enjoy writing more than I used to. Not because it has become effortless, but because the conversation keeps me curious. Instead of experiencing writing primarily as a struggle to make finished sentences, I can experience it as part of the research process itself. More often than not, I finish a session wanting to continue thinking rather than feeling relieved to have stopped.

Bring your own scholarship

One aspect of my practice often surprises people: I almost never begin by asking AI to find my literature.

I do not use AI as a substitute for literature searching, although I sometimes use it later to test whether I have overlooked an adjacent author or debate.  I start by searching for the literature myself. I decide what to read, what is relevant and why it matters. Those decisions shape the intellectual direction of the work, and I want them to remain mine.

I then bring that scholarship into the conversation. I explain why particular papers matter, where I think the gaps are, what puzzles me and what previous research may have overlooked. The quality of the conversation depends heavily on the quality and specificity of what I bring to it. AI is helping me think about scholarship that I have already begun, rather than deciding what my scholarship should become.

I sometimes ask whether other work might speak to the same idea. Occasionally AI suggests an author I already know but had not considered in that context, producing one of those gratifying of course—why didn’t I think of that? moments. But I still locate and read the work myself, and I check that every suggested source actually exists.

If you are an undergraduate or beginning researcher, this advice may sound frustrating because finding literature is precisely where you want assistance. I would nevertheless resist handing over that task. Learning how to search, judge relevance and understand why one source matters more than another are fundamental research skills. If AI selects the literature for you, it also begins to shape the questions you are likely to ask.

Learn to have a conversation

Thinking with AI is not simply about asking better questions. It is also about learning how to respond critically to the answers.

A polished paragraph can create the impression that the work is finished. For me, it usually signals the beginning of a more demanding stage.

I work through revised text paragraph by paragraph and sometimes line by line, asking:

  • Do I actually agree with this?
  • Is this what I would say?
  • Do I fully understand the point?
  • Is the claim supported by the evidence?
  • Has AI overclaimed or removed an important qualification?
  • Is this my judgement, or merely a plausible-sounding sentence?

Sometimes I delete entire paragraphs. Sometimes I rewrite almost everything. At other times, AI has expressed something more clearly than I could initially. The test is not whether a sentence sounds polished. It is whether I understand it, agree with it and am prepared to defend it.

I also spend a surprising amount of time disagreeing with AI. Sometimes it misunderstands what I am trying to say. When that happens, I correct it, but I also ask why the misunderstanding occurred. Was my explanation unclear? Had I skipped a logical step? Was I assuming knowledge that I had not provided? Its misunderstanding can expose weaknesses in my own articulation.

At other times, the disagreement concerns how AI has interpreted someone else’s work. It may describe an author’s argument in a way that sounds entirely plausible but does not match my reading of it. It may emphasise one part of a theory while overlooking the distinction that I think matters most, smooth over a tension that I regard as important, or make two bodies of work appear more compatible than I believe they are.

I do not assume that my interpretation is automatically correct. Instead, I return to the original text and ask what is producing the difference between our readings. Which passages support my interpretation? Have I overlooked something? Is AI relying on a familiar summary of the author rather than the particular argument made in the work I am using? Are we answering slightly different questions?

These disagreements are often extremely productive. They require me to explain not only what I think an author is saying, but why I have interpreted the work in that way and what follows from that reading. Sometimes I revise my interpretation. Sometimes I become more confident that the distinction I am making is necessary. Either way, the process helps prevent theory from becoming a set of familiar names and generic summaries. It makes me return to the substance of the argument.

These disagreements also led me towards a more difficult question: if AI can participate meaningfully in developing an argument, what remains distinctive about the researcher’s role?

One exchange from a paper I am currently writing has stayed with me. AI initially suggested that its role might be understood as contributing to the thinking while responsibility remained with me. That sounded plausible, but it also felt incomplete. Responsibility is sometimes reduced to being the person who signs their name and is held accountable if something goes wrong. It does not fully explain what the researcher contributes to the work.

Working through that discomfort led me to distinguish the roles more carefully. AI can suggest interpretations, identify patterns, test an argument and expose contradictions. The researcher brings the sustained relationship with the question: the reading, evidence, experience and judgement needed to assess those suggestions, as well as the care required to decide what matters and what can responsibly be claimed.

That care is not incidental. I care about the topic I am researching. I care whether the findings are accurate and whether the explanation does justice to the people and situations involved. I care about how the research is interpreted, where it travels and what others may do with it. The work matters to me beyond the moment in which the text is produced.

AI cannot sustain that relationship to the research. It can respond within a conversation, but it does not continue wondering about the problem afterwards. It does not remain answerable to research participants, affected communities or future readers. It has no enduring investment in whether the findings are used carefully, distorted or ignored.

The final responsibility therefore remains with the researcher, but that responsibility is not simply ownership of the text. It rests on the intellectual and ethical work of evaluating, rejecting, revising and standing behind what the paper ultimately says. Contesting the first answer helped me understand more clearly both what AI can contribute and what it cannot.

Once I have a reasonably developed argument, I deliberately change the role I ask AI to play. Instead of asking it to improve the prose, I ask it to challenge the work.

Prompts I return to include:

  • What assumptions am I making here?
  • Where is the argument weakest?
  • What evidence would challenge this conclusion?
  • Where have I overclaimed?
  • What important counterargument have I not addressed?
  • What question do you think I am really trying to answer?
  • Where are the gaps, contradictions or changes in meaning across the paper?
  • Is the paper consistent?

None of these prompts asks AI to generate my argument. They ask it to provide resistance: something against which I can test work that already exists.

I rarely ask AI to tell me what to think. I ask it to help me discover, clarify and challenge what I think.

Finding your voice

A common criticism of AI-generated writing is that it makes everyone sound the same. There is some truth in this. Asked to produce academic prose without sufficient context, AI tends towards smooth, generic language and familiar structures.

But AI cannot give you an academic voice. Your voice develops through reading, writing, teaching, making mistakes and gradually discovering the questions and distinctions that matter to you.

Long before generative AI existed, I started writing GeoFoodie because I wanted somewhere to explore ideas that did not yet belong in academic papers. Looking back, blogging has been one of the most valuable things I have done as an academic. It gave me room to experiment, explain complex ideas to wider audiences and gradually discover how I wanted to write.

It also helped me learn something important: voice is not simply a matter of tone or phrasing. It includes what you notice, what bothers you, which explanations you find inadequate and what kinds of questions you repeatedly return to.

Early-career researchers need some equivalent space. It might be a blog, a newsletter, a notebook or a regular practice of reflective writing. Voice develops through use. It cannot be outsourced.

What AI can do is help you recognise a voice that has already begun to emerge.

One afternoon, almost on a whim, I asked:

“What do you think my research is actually about?”

Looking across thirty years of publications, AI reflected back a pattern that I recognised immediately. Although I had written about entrepreneurship, migration, innovation, food insecurity and community resilience, the same underlying question appeared repeatedly:

What enables or constrains people from doing what they value, and how might those conditions be organised differently?

AI did not discover that research programme. I had spent three decades building it. I already understood that the different areas of my work were connected, but I tended to explain that continuity through the theories I had used. AI helped me express that continuity in language that was simpler, more memorable and easier to communicate to other people.

Later, I asked AI to examine years of my writing and describe how I appeared to think, rather than simply how I wrote. The resulting “voice prompt” was not mainly about turns of phrase. It identified recurring intellectual habits: questioning taken-for-granted explanations, looking for enabling conditions rather than individual deficits and asking what mechanisms connect people, resources and possibilities.

The useful part was not accepting that description as authoritative. It was testing it, refining it and arguing with it until I recognised myself in it.

If you already have a body of essays, blog posts, reports or dissertation chapters, you could ask:

  • What questions do I keep returning to?
  • What assumptions do I repeatedly challenge?
  • What kinds of explanations do I resist?
  • How do I usually structure an argument?
  • What types of examples or contrasts do I use?
  • Which ideas appear to matter most to me?

Do not expect the answers to be definitive. Treat them as interpretations. The value often lies in noticing where you agree, where you resist and what that resistance reveals.

AI does not necessarily make me faster

People often assume that AI makes writers more productive. I am not sure that it has made me faster in any straightforward sense.

Some of my best papers were written long before AI existed and drafted remarkably quickly. Others have taken months or years of revision. AI has not eliminated that variation, nor has it removed the intellectual difficulty of writing.

Its contribution is subtler. It helps me remain engaged with questions that might otherwise become overwhelming or frustrating. Rather than requiring every strand of an argument to be organised internally before I begin, it allows me to develop the structure through dialogue.

For me, this matters more than speed. I do not want to automate away the parts of scholarship I enjoy. I want to protect them.

I enjoy reading across a body of work and suddenly seeing a connection. I enjoy working out why a familiar explanation is inadequate. I enjoy developing an argument until it changes how I understand the problem. I enjoy explaining an idea in a way that enables someone else to see it differently.

What I find difficult is the labour of converting all of that into an orderly structure while trying to hold every part of the argument in my head at once. AI does not create the curiosity or intellectual pleasure. It helps prevent the struggle of communication from overwhelming them.

That is why I hesitate to describe AI primarily as a productivity tool. Productivity is usually understood as producing more in less time. The more important change for me is that it helps sustain attention, reduce avoidance and preserve the joy that made me want to become a researcher in the first place.

Removing barriers

I also use AI to remove barriers around technical tasks that are not central to my scholarship.

For example, I ask it to write Python code for figures and charts. I could not write that code unaided, and becoming a programmer is not one of my research ambitions. AI allows me to describe the analysis and the type of figure I need, generate the code and then refine the result.

This does not remove the need for scrutiny. I still check that the graph accurately represents the data, that labels and scales are appropriate and that the visual does not imply something the analysis cannot support. AI can generate the code, but the interpretation remains mine.

The benefit is not simply that a task takes less time. It is that a technical barrier no longer stands between me and the form in which I want to communicate my research. That leaves more time and attention for the parts of scholarship that require my knowledge and judgement—and that I find most rewarding.

Declaring AI use

Increasingly, journals and universities ask authors to declare how they have used AI. I have found it more helpful to describe the relationship and process than merely list isolated tasks.

Statements such as “AI was used for editing” may be accurate, but they reveal very little about how the technology entered the development of the work. Nor does a list of prompts and outputs adequately represent an extended conversation in which ideas were proposed, challenged, revised and sometimes rejected.

A declaration for work developed in this way might read:

Generative AI was used as a conversational resource during the development of this work to explore ideas, test alternative interpretations, identify connections across previous publications and improve the articulation of the author’s arguments. The author selected and read the literature, interpreted the evidence, developed the theoretical framing, critically evaluated all suggestions and made the final decisions concerning the claims and wording presented.

This explains what AI contributed without pretending that it either did everything or nothing. It also makes the author’s scholarly work visible.

A useful declaration should therefore answer more than What tool did you use? or What prompt did you enter? A useful declaration should explain the role AI played, what changed through the interaction, which suggestions were accepted or rejected, what knowledge was needed to evaluate them and which parts of the scholarly process remained the author’s responsibility.

To me, that is a more honest account of how I think with AI.

A final thought

If you are looking for a shortcut through university, AI may produce text for you, but it will not produce the curiosity, judgement and care through which scholarship develops.

Used uncritically, it can generate fluent nonsense, flatten distinctive voices and make weak arguments appear complete. It can also introduce fabricated evidence, inherited bias and misplaced confidence.

Used critically, however, it can become part of an environment that supports scholarship. It may help someone begin when the blank page feels impossible, stay with a difficult question for longer, see an overlooked connection or recognise a pattern in their own work.

The most important change AI has made for me is therefore not that I can produce more words. It is that I can remain more closely connected to the parts of academic work that I value: curiosity, explanation, intellectual play and the pleasure of gradually understanding something more clearly.

AI has not given me that joy. Research already gave me that.

What it has done is reduce some of the friction that can make the joy difficult to sustain through the long and sometimes frustrating process of turning thought into communicable scholarship.

If AI helps create better conditions for careful, critical and joyful intellectual work, then it has a legitimate place in my research practice.

If it replaces that work, then I have misunderstood both the technology and the nature of scholarship.

Is this Impact?

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I strive to make a positive impact through my work, as many academics do. I also support colleagues to develop their own impact and am often asked by organisations to comment on their plans for creating change.

Graphic of the difference between activity and impact.

So often people think #Impact is what they did—making meals, donating #food or funds, volunteering. They then measure the significance of that activity in terms of its reach: millions of meals served, pounds donated, or volunteer hours.

But that’s not impact.

That’s activity.

The real questions are:

➡️ And then what happened?
➡️ Why does that matter?
➡️ How is the world better because of it?

If the answers to those questions are not compelling, then it may be time to rethink what you’re doing—and how you’re doing it.

This is why, in our recent report on food clubs, we didn’t just ask how many people attended or how much food was distributed. We asked whether food clubs helped people become more food secure, whether they improved diets, reduced anxiety about food, strengthened cooking confidence, and reduced reliance on emergency food support.

Those are the outcomes that matter because they tell us something about whether people’s lives are actually improving.

Measuring activity is important. But measuring impact means asking whether that activity has created meaningful change for the people and communities it was intended to benefit.

I’d be interested to hear: What’s the best example you’ve seen of an organisation moving beyond measuring activity to demonstrating real impact?

Launch of ‘Lets put hunger to bed’: A new campaign from Comic Relief and Sainsbury’s

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Since 2022 I have advised Comic Relief and Sainsbury’s on the Nourish the Nation Campaign. This campaign has now ended, but been replaced by ‘Let’s put hunger to bed’. I was asked to speak alongside Simon Roberts (CEO, Sainsbury’s) and Samir Patel (CEO, Comic Relief) at the celebration event hosted at Sainsbury’s headquarters. I wanted to share my talk.

Paula’s story is one covered in The Bread and Butter Thing’s podcast. These stories from real people who use their food clubs cover a range of topics and are very insightful. The other people mentioned are from interviews being conducted as part of a current research project looking at the impacts of climate induced price inflation on UK households who are already struggling to have the food they need to live thier best lives.

Paula described going from what she called a ‘normal life’ to not being able to buy a birthday present for her granddaughter after her husband suffered a brutal attack that left him unable to work.

Then she said this:

‘By the first Christmas after the attack, we were on our knees. I remember we had a loaf of bread and a packet of chicken crisps for Christmas dinner.’

What struck me about Paula’s story is how quickly an ordinary life can become fragile.

I’m a geographer at the University of Sheffield, and my work looks at how food insecurity is experienced in everyday life — not just as hunger, but as pressure, instability, and the erosion of resilience over time.

For the research with Comic Relief and Sainsbury’s, we surveyed more than 14,000 lower-income households across the UK to better understand the role food clubs play in people’s lives.

And what we found was that food insecurity is rarely a single crisis.

More often, it is a slow wearing down of people’s ability to cope.

A bereavement.
A health problem.
Rising costs.

Hours being cut at work.
Caring responsibilities.

And slowly, the foundations underneath everyday life begin to weaken.

One woman we spoke with —Donna — was in her forties. Her husband died the previous year, and she described struggling deeply with her mental health afterwards. Her adult son had moved back home because he was undergoing cancer treatment. They were both working, but things were still incredibly tight.

Donna also has Type 2 diabetes and needs to eat regularly because she takes insulin.

She talked about how, before joining the food club, she would skip meals so there would be enough food for her grandchildren, who live with them part of the week.

And then she said something very simple:
‘Kids come first, definitely.’

I think that sentence captures something very important about food insecurity in Britain today.

A huge amount of hardship is hidden.

Parents and grandparents absorb it quietly.
They stretch food.
Skip meals.
Keep the heating off.
Manage debts.
Make impossible calculations about what can wait and what cannot.

Donna carefully timed her heating so that when the children were there, they would at least be warm.

She talked about how transport costs could wipe out the money she needed for food.

She talked about making meals stretch with potatoes and soups.

That is not simply budgeting.

That is survival planning.

Again and again in the research, people demonstrated enormous skill, care, and resourcefulness.

One of the central findings from the study is that food insecurity is not simply about a lack of food.

It is what the report calls an ‘architecture of hardship’.

Housing.
Transport.
Health.
Energy costs.
And the constant struggle of trying to hold everything together.

These pressures interact and accumulate over time.

Importantly, the research also challenges a lot of assumptions.

First, people often imagine food insecurity as something that affects people outside of work.

But we heard from nurses, carers, pensioners, parents, and people working multiple jobs.

Second, the issue was not that people did not know how to cook or budget.

Many households experiencing food insecurity were already highly skilled at coping.

The issue was that people were trying to manage impossible pressures for prolonged periods of time.

One of the important findings from the research was that food clubs and food banks are not the same thing — and they are not competing with each other. In the report, I describe this through the Food Ladders approach: different forms of support helping people at different moments of hardship and recovery.

Food banks provide emergency support during an acute crisis.

But food clubs often operate differently.

They provide continuity.
Choice.
Fresh food.
Routine.
Social connection.
And importantly, dignity.

Donna described how the food club meant she no longer had to skip meals herself because there was enough food in the house for everyone.

And because she was eating more regularly, she was better able to manage her diabetes and be there for her son’s children.

That is important.

This is not just about food parcels.

This is about people’s physical and mental health.
Their capacity to cope.
Their ability to care for others.

Another woman, Amy, described how the food club helped her multigenerational household of five women, ranging from aged 8 to 82, maintain access to vegetables and fruit that would otherwise become too expensive.

She talked about cooking collectively, sharing responsibility but also sharing food with other struggling families in the village.

What comes through is not dependency, but active care, skill, and mutual support.

That matters because resilience is social.

One of the strongest findings from the research was what we described as a buffering effect.

Food insecurity damages wellbeing across the board — physically, emotionally, and socially.

But people actively engaged with food clubs often appeared less isolated and more supported than we might otherwise expect, given the pressures they were under.

Both Donna and Amy described the people at the food club as becoming ‘practically a family.’

And I think that language matters.

Now, I do want to say something important here.

Food clubs are not a silver bullet.

No community organisation can solve poverty on its own.

No volunteer network can compensate for inadequate incomes, insecure work, rising housing costs, or weak social safety nets.

And this room reflects something very important: there is no single model that solves food insecurity.

Emergency food aid matters.
Community food projects matter.
Advice services matter.
Schools matter.
Local authorities matter.
Retailers matter.
National policy matters.

Different organisations are responding to different parts of the hardship equation.

But what food clubs help us see is that resilience is not built through food alone.

It is built through relationships.
Through continuity.
Through dignity.
Through creating spaces where people feel recognised and supported rather than judged.

And one of the findings I found most hopeful was that people who engaged with food clubs more regularly and over longer periods were more likely to be food secure.

That matters because it suggests we are not simply seeing emergency relief.

We are seeing the possibility of stabilisation.
Of recovery.
Of rebuilding.

And this matters enormously for children.

Because ultimately, well-fed children depend on well-supported adults and well-supported communities around them.

They need adults who are not constantly exhausted, anxious, isolated or forced into impossible trade-offs.

And that means tackling hunger is not just about responding to emergencies after they happen.

It is about building the conditions that allow people to live with dignity, stability, connection, and hope before a crisis becomes catastrophic.

That is why campaigns like Let’s Put Hunger to Bed matter.

Not simply because they help people eat tonight — though that is vitally important.

But because they help create the foundations from which people and communities can begin to rebuild resilience itself.

Thank you.

Here are the links to both Sainsbury’s and Comic Relief’s materials about the new fund.

You can find the research here, and another blog post where I talk about it a bit more.