Operationalization of intersectionality in physical activity and sport research: A systematic scoping review
Bibliographic record
Abstract
Participation in and opportunities for physical activity (PA) and sports (PA inclusively hereafter) are known to vary across individuals with different social positions. Intersectionality theory may help us to better understand the complex processes of multiple interlocking systems of oppression and privilege shaped by intersections of individuals' social categories. The objectives of this systematic scoping review were (1) to summarize the findings of articles examining PA claimed operationalization of intersectionality and (2) to identify the scope and gaps pertaining to the operationalization of intersectionality in PA research. A search was conducted in September 2019 in seven electronic databases (e.g., SPORTDiscus, Scopus, Web of Science) for relevant research articles written in English. Key search terms included "intersectionality" AND "physical activity" OR "sport". Database searches, data screening and extraction, and narrative synthesis were conducted between September 2019 and May 2020. Of 16564 articles identified, 45 articles were included in this review. The majority of included articles used qualitative methods (n = 41), with two quantitative and two mixed-methods articles. The most frequently observed intersectional social position was sex/gender + race/ethnicity (n = 11), followed by sex/gender + race/ethnicity + sexuality (n = 6) and sex/gender + race/ethnicity + religion (n = 6). Most qualitative studies (n = 38) explicitly claimed operationalization of intersectionality as a key theoretical framework, and over half of these studies (n = 27) implicitly used intra-categorical intersectionality. Two quantitative studies were identified which examined a number of intersections simultaneously using inter-categorical intersectionality. Complex processes of individual and social-structural level factors that drive inequalities in PA opportunities and participation could be better elucidated with the operationalization of intersectionality theory. Intersectionality theory may serve as a useful framework in both qualitative and quantitative investigations. Advancement in quantitative intersectionality is critical in order to produce knowledge that could inform more inclusive PA promotion efforts.
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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 | 0.075 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.040 | 0.037 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".