Women's Rights are Human Rights. To What Extent Does a Country's Feminist Foreign Policy Influence Development Assistance?
Bibliographic record
Abstract
This paper examines the influence of feminist foreign policy (FFP) on official development assistance (ODA) targeting gender equality and women’s empowerment in Canada, France, Germany, Luxembourg, the Netherlands, Spain, and Sweden. A growing number of governments worldwide have adopted FFP in contextually relevant ways, but previous research treats FFP and ODA as two separate concepts. This paper fills the gap in existing research by analyzing the relationship between the two. By combining content and descriptive data analysis, this paper provides a comprehensive understanding of how FFPs influence development assistance. The results show an overall increase in ODA for gender equality and women’s empowerment from 2002 to 2022, and that ODA with a significant objective is consistently higher than ODA with a principal objective. In 2023, Luxembourg, Germany and Sweden were the only countries to meet the 0.7 percent ODA/GNI target set by the United Nations. Spain and the Netherlands are the most supportive of women’s rights organizations and movements, although funding varies from year to year, and smaller, less institutionalized organizations remain underfunded agents of transformative change. Canada and France show that the adoption of FFP leads them to spend more on ODA than before the adoption of FFP, while the other countries do not show similar increases. The findings are limited because FFP is a relatively new policy concept, making it difficult to assess the influence of FFP on ODA. However, this paper provides an entry point for future research on the contextual adoption of FFP and its specific influence, particularly in the realm of development assistance.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".