Combining feminist intersectional and community-engaged research commitments: Adaptations for scoping reviews and secondary analyses of national data sets
Bibliographic record
Abstract
As Hankivsky & Cormier (2011) and Denis (2008) note, the theoretical evolution of intersectionality has outpaced its methodological development. While past work has contributed to our understanding of how to apply intersectionality in research (CRIAW-ICREF & DAWN-RAFH 2014; Morris & Bunjan 2007; Simpson 2009), gaps persist. Drawing on a four-year community-university research collaboration called ‘Changing public services: Women and intersectional analysis’, we explore the incorporation of feminist intersectional and community-engaged research commitments into secondary data analyses, specifically a scoping review and secondary analyses of two Statistics Canada data sets. We discuss our application of these commitments across all stages of designing and undertaking these analyses, in particular drawing into focus the importance of dialogue and deliberation throughout our process. Our application of feminist intersectional and community-engaged commitments – including prioritising community benefit and practising self-reflexivity – revealed gaps and silences in the data, in turn improving our understanding of differences in people’s experiences, our critiques of policies and our identification of new research questions. The lessons learned, we conclude, are valuable for scholars, whether or not community engagement is central to their scholarly commitment. Keywordsfeminist intersectionality, community-engaged research, scoping review, logistic regression, community-university partnerships, Canadian public services
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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 | 0.572 | 0.617 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.055 | 0.066 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.010 | 0.032 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".