An Urgent Need for Quantitative Intersectionality in Physical Activity and Health Research
Bibliographic record
Abstract
The term intersectionality was originally introduced by Crenshaw 1 to highlight how Black women's experiences were marginalized via erasure within feminist movements.Using intersectional analysis, Crenshaw and others identified the modal "woman's experience" of oppression as a quintessentially White woman's experience in the absence of any consideration of race, as was common in early second-wave feminist organizing.Since Crenshaw's initial formulation, intersectionality has been extrapolated beyond gender and race to consider how many individual factors intersect with and impact each other in the lives of individuals and communities.Today, intersectionality is a theoretical and methodological framework for understanding the ways in which gender identity, gender expression, race/ethnicity, disability, class, age, and other social identities interweave in their impact on health and well-being.2,3 Acknowledging that interwoven forms of marginalization cannot be reduced to one particular factor, an intersectional approach considers how simultaneously belonging within multiple marginalized groups informs how one is impacted by marginalization.4 Intersectionality in Physical Activity ResearchEvidence from White settler colonies, such as Australia, Canada, South Africa, and the United States, as well as racially and ethnically diverse parts of the world consistently suggests that participation in regular physical activity, including sport, is the highest among well educated, wealthy, cis-heterosexual White, able-bodied men above all others.5,6 Barriers to physical activity participation in relation to race/ethnicity, gender identity and expression, ability, class, and other social position factors have predominantly been investigated using qualitative methods.7 This qualitative preponderance is partly attributable to a premise of intersectionality that social positions and relevant experiences cannot neatly fit into ordinal scales for metric statistical analysis.8 Nonetheless, one recent scoping review investigating the operationalization of intersectionality in physical activity research 9 suggested that intersectionality may also serve as a useful framework in quantitative research.In particular, elucidating complex processes of individual-and social-structural-level factors that drive inequalities in physical activity participation in populationbased, large-scale surveys could better inform more inclusive physical activity promotion policies and programs.In addition to the need for quantitative research applying intersectionality, the authors 9 of the review also suggested that intersectionality-based investigation in physical activity contexts has largely been limited to investigating the sex/gender + race/ethnicity dyad; thus, investigating varying axes of marginalization beyond the sex/gender + race/ethnicity dyad is important.Two recent reviews of qualitative studies examining LGBTQ+ adults' intersectionality-based experiences in sport settings 10,11 also highlighted that more intersectional research is required to better understand how more individuals with different memberships within marginalized groups can have quality opportunities and experience in physical activity.More recently, Joseph et al's 12 scoping review on racialized women in sport in Canada reiterated the importance of considering intersectional identities, suggesting that there is a lack of or no evidence investigating intersectionality-based experiences in sport beyond the sex/gender + race/ethnicity dyad (eg, no studies found focusing on the experience of sport among racialized trans women).
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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 | 0.299 | 0.291 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.020 | 0.025 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.028 | 0.057 |
| Open science | 0.010 | 0.035 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".