Academic-community collaboration, gender research, and development: pitfalls and possibilities
Bibliographic record
Abstract
Collaboration has become a watchword for development practitioners and theorists. Yet collaboration or partnerships between academics and community-based researchers and activists have often proved difficult. This is particularly true for partnerships with smaller, grassroots community researchers, who are generally less resourced than their academic partners. This paper focuses on such partnerships in gender research, with the aim of reflecting on past problems as well as successes in order to develop strategies for making such projects more truly collaborative, rather than a minefield of broken promises and unspoken (and sometimes spoken) resentments.
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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 | 0.211 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.023 | 0.129 |
| Scholarly communication | 0.029 | 0.049 |
| Open science | 0.005 | 0.035 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".