Gender-Based Analysis Plus in Canada: Problems and Possibilities of Integrating Intersectionality
Bibliographic record
Abstract
International debate is ongoing regarding the innovation of gender mainstreaming (GM), its efficacy, and future utility. Likewise, in Canada, there is a push to learn from early GM efforts and a renewed focus on creating more responsive mainstreaming strategies. Although Canada’s gender-based analysis (GBA) has been researched and evaluated, this study uses the feminist theory of intersectionality to examine the newer GBA+ model, which builds on its predecessor. Drawing on thematic analysis of 32 stakeholder interviews from three different sectors and content analysis of key policy reports, we investigate how the shift to this new model is perceived, whether inclusion of the “+” results in greater responsiveness, and how to better operationalize intersectionality in policy contexts.
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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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.034 | 0.023 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".