‘Doing’ or ‘using’ intersectionality? Opportunities and challenges in incorporating intersectionality into knowledge translation theory and practice
Bibliographic record
Abstract
Intersectionality is a widely adopted theoretical orientation in the field of women and gender studies. Intersectionality comes from the work of black feminist scholars and activists. Intersectionality argues identities such as gender, race, sexuality, and other markers of difference intersect and reflect large social structures of oppression and privilege, such as sexism, racism, and heteronormativity. The reach of intersectionality now extends to the fields of public health and knowledge translation. Knowledge translation (KT) is a field of study and practice that aims to synthesize and evaluate research into an evidence base and move that evidence into health care practice. There have been increasing calls to bring gender and other social issues into the field of KT. Yet, as scholars outline, there are few guidelines for incorporating the principles of intersectionality into empirical research. An interdisciplinary, team-based, national health research project in Canada aimed to bring an intersectional lens to the field of knowledge translation. This paper reports on key moments and resulting tensions we experienced through the project, which reflect debates in intersectionality: discomfort with social justice, disciplinary divides, and tokenism. We consider how our project advances intersectionality practice and suggests recommendations for using intersectionality in health research contexts. We argue that while we encountered many challenges, our process and the resulting co-created tools can serve as a valuable starting point and example of how intersectionality can transform fields and practices.
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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 | 0.358 | 0.281 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.028 | 0.218 |
| Scholarly communication | 0.069 | 0.113 |
| Open science | 0.010 | 0.069 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".