Politically informed approaches to working on gender equality in fragile and conflict-affected contexts
Bibliographic record
Abstract
Gender inequality and political power relations are inextricably linked, and are especially complex in fragile and conflict-affected settings. This policy paper provides practical recommendations for donors and practitioners on how to integrate gender equality into programming in fragile and conflict-affected contexts using politically informed approaches. It goes beyond traditional development practices and ways of working, as well as the main systems, practices, and tools required to implement politically informed approaches. The paper emphasises the importance of integrating analysis of power relations and the functioning of political and socio-economic systems; and the important role of negotiating barriers and using opportunities within existing systems in order to achieve the desired change – with the potential to transform both gender inequalities and fragility, which is key to achieve the sustainable development goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.042 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".