‘Doing’ or ‘using’ intersectionality? Opportunities and challenges in incorporating intersectionality into knowledge translation theory and practice
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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