Built on shaky ground: Reflections on Canada’s Feminist International Assistance Policy
Bibliographic record
Abstract
In October 2017, Canada launched its Feminist International Assistance Policy (FIAP). While Canada’s explicit use of the words “feminist” and “feminism” may be refreshing, critical questions on the FIAP’s interpretation and application of these concepts remain. These challenges are not unique to the FIAP. Rather, the central weaknesses of the FIAP can be seen as symptomatic of several endemic challenges that persist in the current policies and practices that seek to promote gender equality in the developing world and beyond. This article presents the theoretical and conceptual lineage that has informed the FIAP, drawing from challenges present within literature on security, gender equality, and gender mainstreaming. Three main shortcomings relevant to both the literature and the FIAP are explored: first, the assumptions and essentialization of “gender” to mean “women ”; second, the frequent conflation of “gender equality” with “women’s empowerment”; and last, the paradox of gender, gender equality, and feminism being simultaneously over-politicized and depoliticized to suit prevailing policy environments, with particular implications for the global coronavirus pandemic, as well as impacts in fragile and conflict-affected states. This analysis sheds light on persistent challenges in feminist foreign policymaking and offers insights for the development of Canada’s White Paper on feminist foreign policy.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.056 | 0.038 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".