Combining feminist intersectional and community-engaged research commitments: Adaptations for scoping reviews and secondary analyses of national data sets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.121 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it