30 Apr 2026 - Amy
So, just when I was starting to get back up this month, the universe has said “no, Amy, not today”, and my aunt died. In fact, I write this blog post on the train back to Southampton to attend her funeral. So I once again have not had the brilliantly productive month I had planned. This means, once again, we have a slightly rogue blog post just on something I’ve been thinking about. Which is, with the local elections coming up, my politics, and how it embodies everything I am, and everything I do.
I am a socialist, and I carry that commitment into how I do science. Not as a slogan, and not always loudly (although sometimes loudly, if strikes are afood), but in the everyday decisions that shape who gets access to knowledge, whose labour is valued, and how benefits and costs are distributed. In physical geography, these choices are often framed as technical or administrative. I think it matters that we recognise them as inherently political, and demanding acknowledgement.
Research does not happen in a vaccuum, and whilst the time periods I study are not inherently political given that human society 130,000 years ago was nothing like modern society, but the time period I am studying in absolutely is. Therefore, it matters to me that I, and my peers, remembmer knowledge is a collective good, produced through social labour, and it should be governed in ways that minimise exclusion and exploitation. Science is often spoken of as neutral or universal, but its institutions are anything but. Publish‑or‑perish cultures, prestige hierarchies, and closed data infrastructures reward accumulation by individuals and organisations that already have resources. Practising socialism within science therefore looks less like grand gestures and more like resisting, where possible, logics of enclosure and scarcity.
One expression of this is a strong commitment to open data and open methods. Environmental data are typically produced using public funds and through the diligent and ceaseless work of technicians, students, assistants, and communities who are rarely credited as knowledge producers. Keeping those data locked behind paywalls or informal gatekeeping reproduces inequality under the guise of quality control. Openness is not a panacea—there are real issues around misuse, misinterpretation, and extractive re‑analysis—but treating openness as a default shifts the burden of justification. The question becomes not “why share?” but “what harms might sharing cause, and how can they be mitigated?”. That is a more honest starting point.
A second, and harder, practice concerns labour. Academia routinely relies on unpaid or underpaid work: short‑term contracts, casual teaching, “opportunities” that substitute for wages, and an expectation that care, mentoring, and diversity work will be absorbed invisibly. As a socialist, I try to resist benefiting from that exploitation. Where I can, I pay research assistants properly, budget for their time rather than treating enthusiasm as compensation, and push back against norms that present precarity as a rite of passage. I do not pretend this resolves structural problems. It does, however, make visible that labour has a cost, and that choosing not to pay it is still a choice.
Supporting scholars from non‑typical backgrounds is also fundamental. Academia often attributes inequality to individual deficit—confidence, polish, networking—rather than to structural barriers shaped by class, race, disability, caring responsibilities, or migration status. I would not have got to where I am today (and I’ll remind you here that I am still not permanantly employed, but I feel blessed to have a 3-year contract that provides something close to stability), without a lot of luck and a lot of support and patience. Practising socialism here is less about mentoring as benevolence and more about redistribution of risk and opportunity. That might mean sharing data that took time to collect, putting time and effort into activities that might not result in classical academic markers of success (I put a great deal of time into training others, for example) or being explicit about the informal rules that are often left implicit. These are small acts, but they counter the fiction that success is purely meritocratic.
I am not always a perfect socialist academic. I work within systems that reward competition and accumulation, and I benefit from them. My own commitments are shaped by what I can afford to refuse. A socialist practice of science is therefore necessarily partial and contradictory. I try hard, however, to recognise and acknowledge my limitations, because it guards against moralising and against the fantasy that better behaviour by individuals can substitute for institutional change.
If any of this resonates, I’d invite you to look closely at your own research practices and ask where small redistributions are possible. Consider who can access your data, whose labour you rely on, and who bears the risks of precarity in your projects. None of us can remake academia alone, but making these choices visible—and contestable—in our everyday work is one way of aligning how we do science with the values we often claim to hold.
I am convinced that how we do science matters as much as what we find. Data policies, authorship norms, and pay decisions encode values whether or not we acknowledge them. Naming my socialism is a way of making those values explicit, and of holding myself accountable to them in the everyday conduct of research.
Enjoy the long weekend, I’ll see you next month.