Gender Data, Intersectionality, and a Feminist Politics of “Negotiated Refusal”
Bibliographic record
Abstract
Gender and intersectional data are recognized as vital to addressing gender-based violence. We engage this thesis through a case study of a gender data project at the Colombia-Venezuela border. Coming from an underexplored vantage point in the literature, we trouble the assumption that more data are always better for advancing feminist objectives around GBV. We advance the concept of "negotiated refusal" to make sense of the decision of the project's frontline implementers to collect less data. We argue that the complex character of inequalities and the dynamic nature of context requires flexibility in what gender and intersectional data should consist of and that top-down frameworks may ultimately prove counter-productive to gender equality efforts.
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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 | 0.107 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.029 | 0.121 |
| Scholarly communication | 0.027 | 0.029 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".