Food resilience is a two-sided platform problem

There is growing recognition that the UK needs to build greater food resilience. Tim Lang’s recent work is important here. It makes a compelling case for readiness: shorter supply chains, diversified sources of production, and stronger capacity to withstand shocks. That matters enormously. But readiness on the supply side is only half the story.

Food resilience is a two-sided platform problem.

It is not enough to ensure that food exists, or even that it reaches shops, distribution hubs, or community outlets. We also have to ask whether people can access that food in ways that allow it to become nourishment, care, and everyday security. Just because food is available does not mean it will get to the people who need it. And even if it does, that still does not guarantee it can be stored, cooked, shared, or eaten.

This is where community resilience comes in. As I argue in Building Resilience: The Role of Food Clubs in UK Food Security, food security is not only about what is in the system. It is also about whether people have access to community-based infrastructures that allow food to be obtained, stored, cooked, shared, and eaten in ways that support everyday life. Food clubs are one example of this broader resilience architecture.

In economically wealthy contexts such as the UK, the dominant mechanism through which people access food is through purchase in a market system organised primarily around profit maximisation. For many people, this works well enough most of the time. But it is also a fragile arrangement. It assumes that households have enough money, enough time, enough equipment, enough energy, enough transport, enough storage, and enough practical capacity to turn food into meals. When any of these are disrupted, access breaks down, even when food is technically present in the system.

That is the blind spot in many discussions of food resilience. We talk about supply, but not enough about access. We talk about availability, but not enough about use.

A resilient food system therefore needs more than diversified production. It also needs diversified consumption mechanisms: multiple ways for people to obtain and use food beyond the narrow logics of maximising sales and extracting profit. This may still include purchase, but through models where surplus supports sustainability rather than endless growth. It may also include sharing, gifting, barter, mutual aid, community growing, food clubs, social eating spaces, and other collective infrastructures of access.

Amartya Sen helps us think about this differently. What matters is not only whether food exists as a commodity, but whether people have real opportunities to access it through different means. These could include buying, but also sharing, gifting, own production, barter, or community exchange. I think of these as access channels: the practical routes through which food becomes available in everyday life.

This matters because highly “efficient” systems are often only efficient from the perspective of profit. They may be efficient at moving products, cutting slack, and concentrating market power, while being deeply inefficient for people, place, planet, and even food itself. If food is produced and distributed in ways that cannot be reliably turned into sustenance where it is needed, then the system is not truly resilient.

Building alternative access channels does more than help people at the margins. It strengthens the whole system. When households and communities have multiple ways to access food, they are less exposed to shocks in any single channel. And when non-maximising forms of provision exist alongside profit-driven ones, they also put pressure on the mainstream system to respond differently. They force greater attention to health, wellbeing, justice, and sustainability.

So yes, we need shorter supply chains and diversified production sources and methods. But that is not sufficient. We also need community resilience and diversified access channels. Food resilience is not just about making sure food is there. It is about making sure people can actually get it, use it, and benefit from it.

If we forget that, we risk building a food system that is ready for disruption in theory, but not resilient in practice.

Beyond Hunger: How Communities Fight Back Against Food Insecurity and Neoliberalism a podcast

I have been playing around with Google’s Notebook LM. One feature is the audio overview option. This produces a podcast-type audio discussion that reviews the documents that you ask it to. For this podcast, I asked it to do this for my paper, More than Just Food, which I published in 2019. I was really impressed with the outcome. The podcast lasts about 16 minutes. Have a listen. What do you think?

Narrating the power of food clubs with AI

In a previous post, I talked about using Gemini to explore the impact of my research. Today, I am going to explore that research with a different tool:  Notebook LM. The big difference between Gemini and Notebook LM is that Gemini searches the web, and as I found, not always so successfully, while Notebook LM looks just at what you ask it to.

So I did an experiment. I put the same paper I used for the Gemini experiment, a report I am working on right now (to be released soon, so watch this space), the recently published UK national food strategy, some recent research on Food Banks by the Trussell Trust, and a link to the food ladders discussion in this blog. One of the features of Notebook LM is that it can produce a video summary of the things you ask it to look at and present it in a narrated slide show. Here is what it produced. What do you think?

At present, you don’t have the option to change the accent (sorry, and that is not me). I think it does quite a good job of juggling across a number of quite dense sources. If you want to digest things quickly, it’s a good option. There are some nuances that are missing, but this seems reasonable for a 6-minute video. What is missing from this is what happens at rung three, and where we go once we leave the food club? I’m not sure we’ve cracked that one yet.

If you want a deeper dive, Google’s Notebook LM also allows you to ask for an audio overview that lasts about 20 minutes. It does this in a rather entertaining interview style. It is worth trying out. You still need to read the documents to get the nuance, though.

Using AI to understand the influence of research

I have become a fan of Google’s AI tools. They are fantastic for summarising and revising text to make it clear, concise and well-formed. There are some limitations, of course. Gemini, for example, can make up sources, and it cannot access everything that one might think of as being ‘open access’. Notebook LM is great for cutting across articles and notes that the user feeds directly into the notebook. This helps bypass the making things up element. While Gemini seems to be better at producing concise text, Notebook LM can be a bit verbose; both have their uses.

I thought I would try an experiment with a paper that I am very familiar with because I wrote it. I wanted to see what sort of results I could get from Gemini on the influence or impact of the paper. The paper that I was exploring was my 2019 paper, “More than Just Food: Food Insecurity and Resilient Place Making through Community Self-Organising,” published in Sustainability (https://www.mdpi.com/2071-1050/11/10/2942).

Gemini’s process

Using the deep research function, I asked Gemini, “How has this paper been used by other researchers to shape their research?” and included a link to the paper. The benefits of the deep research function are that it tells you what the thinking process is and then produces a report based on what it finds. The PDF linked shows all the ‘thinking’ Gemini did to arrive at a final report. A few immediate observations about this. Firstly, Gemini does an excellent job cutting to the key contributions of the paper. Secondly, I knew pretty much where to look, but it encountered access problems, which are likely to limit the ability of Gemini to provide clear and meaningful answers to prompts that students or other researchers may have about a particular research topic. Thirdly, Gemini does try to find workarounds that seem plausible; however, again, it encountered access problems.

The report it produces

This is the report that it produces from its somewhat limited ability to access citing literature, despite this literature being mostly open access. The first interaction did not include the (somewhat difficult to read) table at the end, but when I pointed this out, the table was added. You have the option to export the report as a Google Doc, which is really handy. What the report doesn’t do is what I wanted it to do, which was a review of all the papers that cited my paper. It does show where the contributions of the paper to the literature are, but not specifically how my work is being used. However, it is still nice to have a clear summary of not just what the contribution is, but also how it is a contribution. It is also really positive, which is a bit of an ego boost.

This summary of my research also tells me some other things about my own research. Given the number of contributions that it finds to a whole range of areas–something that is inherently a problem linked to my Dyslexic mind, as for me it is all interconnected. I clearly need to work on limiting the ways that I seek to make my research publications relevant by focusing on making one or maybe two contributions if I want others to use the work in their research. Too many contributions make it hard for others to see what is most important and then use that centrally in their own work.

AI for Research: A Realistic Look

My experiment with Gemini AI offered a fascinating look into how these tools gather and present information, revealing both their strengths and their current limitations when it comes to assessing a paper’s impact.

It clearly shows that AI tools, like Gemini, excel at providing quick summaries and pinpointing a paper’s main arguments, giving you a valuable head start in understanding its essence. AI can also help you understand a paper’s broader thematic contributions – how its central ideas resonate and are adopted in wider academic discussions. This encourages a more conceptual way of thinking about how research influences a field, moving beyond simply counting citations.

However, these tools are not perfect. It’s crucial to always cross-reference information, be aware of potential ‘hallucinations’ (where the AI invents facts or sources), and recognise that AI may not have access to all relevant literature, even if it’s publicly available. While AI is a powerful tool, it doesn’t replace the need for researchers to master traditional, comprehensive literature search strategies. Interestingly, observing Gemini’s ‘thought process’ can even offer students a blueprint for developing their own effective search strategies using academic databases like Google Scholar, Web of Science, or Scopus. This combination ensures both thoroughness and accuracy in your research.  

Finally, using AI like Gemini can help you refine a paper’s core contribution. By summarising its perceived impact, it can highlight if a paper’s scope is too broad or if it attempts to make too many distinct contributions. For greater impact and easier adoption by other researchers, focusing on one or two central, clearly articulated contributions per publication can make your work more digestible. You can even use AI prompts to help revise your writing for better focus. It might also reveal thematic connections you hadn’t considered, sparking new ideas for future research.