Gender Justice and Feminist Politics: Decolonizing Collaborative Research
Bibliographic record
Abstract
The most prominent social effects of the drug war in Mexico are the criminalization of poverty and increased rates of feminicide. Feminist academics and community leaders have been developing and working hand in hand to find the most appropriate methods to document gender-based violence and feminicide to shed light on the impunity that hides the systemic dismissal of women’s lives. This essay presents a critical analysis of my own positionality as a feminist and academic ally in building a collaborative research alliance with indigenous women leaders who are politically engaged in the production of knowledge from an intersectional perspective that adequately reflects the matrix of violence that affects the lives of indigenous women in urban and rural areas. This process has been fruitful and promising, although it has also entailed challenges and contradictions arising from disparate meanings of gender justice and the lack of encounter of feminist/indigenous politics of resistance.
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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 | 0.096 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.032 | 0.175 |
| Scholarly communication | 0.030 | 0.026 |
| Open science | 0.004 | 0.040 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".