Bibliographic record
Abstract
In the face of continuing gender inequalities and violations of women's rights, questions are being asked as to whether the predominant approach used by donor organizations—gender mainstreaming—remains a viable approach. At the same time, over the last few years, development practitioners' attention has been turning to the international human rights framework, and specifically, what is referred to as the “rights-based approach” (RBA) or “human-rights based approach” to development, linking both human rights practices and principles to international development approaches. This article explores the concept of an RBA to determine if it provides a useful methodology for furthering progress by donor agencies on achieving gender equality and women's rights. It is argued that for gender equality advocates working in donor organizations, an RBA adds value to current gender main-streaming efforts. However, a number of issues and lessons learned from gender mainstreaming need to be addressed to ensure that gender equality and women's rights are not marginalized.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.069 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".