Feminist Infrastructure for Better Weathering
Bibliographic record
Abstract
Big infrastructure responses to climate change seek to protect the heteropatriarchal capitalist status quo. In contrast, this article develops a theory and method of practice-led research to facilitate better weathering. In so doing the article contends that a transformative feminist response to climate change needs alternative, collective, feminist infrastructures. The feminist specificity of the infrastructure proposed here emerges through its proximity to the concept ‘weathering'. As a feminist figuration, weathering attunes us to human embodiment and difference in a time of climate change, where ‘weather' is not only meteorological, but the total atmospheres that bodies are made to bear. An infrastructure for better weathering thus centres opportunities to acknowledge and account for embodied difference and the differential effects of weather as a specifically feminist design feature. Better weathering is not neoliberal resilience, but rather attention to and redistribution of low-stakes vulnerability as an infrastructural politics. The article proceeds in two parts. We theorise a feminist infrastructure. We then pilot the infrastructure in a series of practice-led research activities. We argue these new infrastructures facilitate low-stakes vulnerability between strangers and so enable better weathering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".