MétaCan
Menu
Back to cohort
Record W4224286595 · doi:10.3390/sports10040055

Kinesiology, Physical Activity, Physical Education, and Sports through an Equity/Equality, Diversity, and Inclusion (EDI) Lens: A Scoping Review

2022· review· en· W4224286595 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSports · 2022
Typereview
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInclusion (mineral)IndigenousEquity (law)KinesiologyDiversity (politics)QueerTransgenderSociologyPublic relationsPhysical educationPolitical scienceGender studiesPsychologyPedagogyMedical educationMedicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.867
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0070.000
Scholarly communication0.0000.001
Open science0.0010.031
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.377
GPT teacher head0.598
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it