Kinesiology, Physical Activity, Physical Education, and Sports through an Equity/Equality, Diversity, and Inclusion (EDI) Lens: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Equity, equality, diversity, and inclusion are terms covered in the academic literature focusing on sports, kinesiology, physical education, and physical activity, including in conjunction with marginalized groups. Universities in many countries use various EDI policy frameworks and work under the EDI headers "equality, diversity and inclusion", "equity, diversity and inclusion", "diversity, equity and inclusion", and similar phrases (all referred to as EDI) to rectify problems students, non-academic staff, and academic staff from marginalized groups, such as women, Indigenous peoples, visible/racialized minorities, disabled people, and Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, and Two-Spirit (LGBTQ2S+) experience. Which EDI data, if any, are generated influences EDI efforts in universities (research, education, and general workplace climate) of all programs. METHOD: Our study used a scoping review approach and employed SCOPUS and the 70 databases of EBSCO-Host, which includes SportDiscus, as sources aimed to analyze the extent (and how) the academic literature focusing on sports, kinesiology, physical education, and physical activity engages with EDI. RESULTS: We found only 18 relevant sources and a low to no coverage of marginalized groups linked to EDI, namely racialized minorities (12), women (6), LGBTQ2S+ (5), disabled people (2), and Indigenous peoples (0). CONCLUSIONS: Our findings suggest a gap in the academic inquiry and huge opportunities.
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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 | 0.028 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.046 | 0.040 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| 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".