WE1.2: Gender Based Analysis Plus: A strengthened approach to gender integration and intersectionality
Bibliographic record
Abstract
The Government of Canada has been using Gender Based Analysis (GBA) as an analytical tool to support the development of more responsive and inclusive initiatives for over 25 years. The approach has changed over time, moving from ‘GBA' to ‘GBA Plus' to signify the range of identity factors beyond gender (such as age, race, religion, disability, socioeconomic status, geographical concerns, etc) that constitute inequality and need to be integrated in analyses to support more inclusive policies. An even more recent iteration of the GBA Plus approach emphasizes the role of social relations, structures and systems of oppression for producing and maintaining inequalities. Together these changes and the resulting tools are enabling researchers, analysts and policymakers alike to develop and engage with deeper and more intersectional social analyses. This session will introduce participants to the strengthened approach to GBA Plus, highlight useful and accessible GBA Plus guidance and tools, and identify ways the GBA Plus approach could support more intersectional and socially inclusive agri-food system research and policies. During the session there will be space to discuss the challenges and limitations of using a ‘gender-first' or additive approach to intersectionality and to highlight an example of how external partners (in this case, Canada's main Indigenous women's organisations) develop and use their own GBA Plus frameworks tailored to their respective 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 | 0.018 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".