Moving beyond jargon: Operationalising gender-transformative approaches to end harmful practices against adolescents
Bibliographic record
Abstract
The UNFPA-UNICEF Global Programme to End Child Marriage and the Joint Programme on the Elimination of Female Genital Mutilation aim to contribute to SDG target 5.3 on the elimination of harmful practices against women and girls through gender-transformative approaches (GTAs). In collaboration with Collective Impact, the Global Programme developed and implemented the Gender-Transformative Accelerator tool, a workshop-based rapid gender assessment and planning process for country offices and implementing partners to advance operationalisation of GTAs. The Accelerator was rolled out in 15 countries in Sub-Saharan Africa, South Asia, and the Middle East from 2021 to 2023. Looking across country contexts, this case study describes the Accelerator approach, implementation, key successes, challenges, and early outcomes. Workshop values clarification activities enabled staff to reflect critically on their social contexts and gender- and age-related biases, and deepened the resonance and relevance of GTAs. This, in turn, facilitated productive critical assessment of programmes and development of action plans to advance GTAs at multiple socio-ecological levels. The case study concludes with lessons learned and the path forward for implementing the Accelerator and operationalising GTAs to promote the rights, health, and wellbeing of adolescent girls and boys in all their diversity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".