Preparing the next generation for translating feminist and gender equality policies into practice
Bibliographic record
Abstract
Since the Government of Canada's adoption of a range of feminist and gender-based commitments including the Gender-based Analysis Plus (GBA+) framework in 1995 and the Feminist International Assistance Policy in 2017, the need for highly trained practitioners with skills in feminist theory and methodology is increasingly clear. In this article we consider why the next generation of changemakers (future policymakers, analysts, researchers, and practitioners) need highly advanced skills in feminist data collection, analysis, and evaluation. Based on our evaluations of training methodologies, toolkits, and analysis of evaluation programs, we explore what that training should require. With this context in mind, we consider whether graduates from post-secondary programs that serve as major training platforms for working in government and civil society organizations receive sufficient training in these programs to apply feminist critiques, analyses, and skillsets to engage in work that centers feminist principles and gender-based analysis priorities.
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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 | 0.258 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".