Integrating Intersectionality in Policy: A Review and Analysis of Guidance
Bibliographic record
Abstract
Addressing inequities is a pressing policy priority, particularly with growing awareness of interlocking global challenges, including pandemic recovery. Although Canada has been a leader in developing intersectionality-informed policy approaches, interest in intersectionality is growing internationally. Yet, misunderstandings persist on how to operationalize intersectionality, and calls are being made to develop clear and effective guidance to do this. This article presents results of a scoping review of policy-relevant guidance on operationalizing intersectionality, including tools, guides, and frameworks. The findings include an analysis of where such work is being produced, whether guidance to date aligns with the key tenets of intersectionality, examples of promising practices, and approaches that fall short of effectively operationalizing intersectionality. This is the first review of its kind internationally and provides insights into what is required in guidance to ensure the value added of intersectional analysis in working toward mitigating inequities and promoting social justice.
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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 | 0.068 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.034 | 0.053 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".