COVID-19 and gender-based violence: reflections from a “data for development” project on the Colombia–Venezuela border
Bibliographic record
Abstract
Click to increase image sizeClick to decrease image size Disclosure statementJulia Margaret Zulver, Tara Patricia Cookson, and Lorena Fuentes work for Ladysmith, a feminist research consultancy. With USAID and Global Affairs Canada funding, Ladysmith designed and implemented the Cosas de Mujeres intervention, whose data is reviewed in this paper. The research presented here did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Additional informationNotes on contributorsJulia Margaret ZulverJulia Margaret Zulver is a Senior Researcher at Ladysmith, where she is also the Director of Field Operations for Gender Data Kit. She is a Marie Skłodowska-Curie Research Fellow at the University of Oxford, UK, and the Universidad Nacional Autónoma de México, Mexico.Tara Patricia CooksonTara Patricia Cookson is the co-founder and Director of Research at Ladysmith. She is also an Assistant Professor of Gender and Development in the School of Public Policy and Global Affairs at the University of British Columbia, Canada.Lorena FuentesLorena Fuentes is the co-founder and Director of Practice and Advocacy at Ladysmith. She is also a Lecturer in Gender and Development Studies at the International Institute at the University of California Los Angeles, USA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".