GeJuSTA Gender Analysis and Gender-Just Co-Design Toolkits
Bibliographic record
Abstract
Work in the field of digital development has often been carried out without sufficient attention to gender justice. Indeed, where researchers or practitioners have been sensitive to gender, this has often only extended to “counting women” - gathering data on how many participants of which gender participated in their interventions. We worked together with women and women’s rights activists in South Africa and Uganda to co-produce conceptually advanced yet practical toolkits for researchers and practitioners in our field: (1) To deepen their gender analysis and improve their understanding of the barriers women and other disadvantaged groups face when seeking to engage with digital innovation (Gender Analysis Toolkit) and (2) To engage women and other disadvantaged groups actively in the co-design on digital innovations (Gender-Just Co-Design Toolkit). We present the the toolkits as Open Access and freely available references for research, policy and practice.
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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 | 0.034 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.010 |
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".