The Operationalizing Intersectionality Framework
Bibliographic record
Abstract
Intersectional approaches are needed in sport research and administration to create significant changes in access, participation, and leadership. The operationalizing intersectionality framework—graphically represented as a wheel with spokes and points of traction—offers a nonexhaustive, evolving structure that can facilitate contextual, deliberate actions to disrupt overlapping systems of oppression. The framework was assembled to guide E-Alliance, the gender equity in sport in Canada research hub, in embodying its commitment to intersectional approaches and designed for broader application to sport. Current gender equity efforts mostly continue to prioritize the knowledge and needs of White, middle–upper-class, nondisabled, not fat, heteronormative, binary, cisgender women and have yet to achieve parity. Acting meaningfully on commitments to intersectional approaches means focusing on how axes work together and influence each other. The framework can help advance cultural sport psychology and ultimately improve athletic well-being.
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.
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 | 0.036 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.011 | 0.061 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".