An infrastructural politics for climate change adaptation, via weather: Interview by Enrico Campo with Jennifer Mae Hamilton and Astrida Neimanis
Bibliographic record
Abstract
Jennifer Hamilton (Senior Lecturer in English Literary Studies), and Astrida Neimanis (Canada Research Chair in Feminist Environmental Humanities) are environmental feminist scholars who explore the relationship between weather, climate change, and infrastructure. They co-authored How to Weather Together: Feminist Practice for Climate Change (Bloomsbury Academic, forthcoming 2026), which includes a chapter on infrastructure. Together with artist and writer Tessa Zettel, are part of the Weathering Collective ( https://weatheringstation.net/ ), a project that experiments with collaborative practices between theoretical and critical research and artistic practice. With Zettel, they co-authored ‘Feminist Infrastructure for Better Weathering’ for Australian Feminist Studies . This interview explores the concept of ‘weathering’ and how and why they argue for an urgent need to think critically about adaptation in relation to infrastructure in order to address the challenge of climate change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".