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Record W2305938735 · doi:10.1093/heapro/daw009

Inter-categorical intersectionality and leisure-based physical activity in Canada

2016· article· en· W2305938735 on OpenAlexaffabout
Hayfa Abichahine, Gerry Veenstra

Bibliographic record

VenueHealth Promotion International · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of British ColumbiaProvincial Health Services Authority
Fundersnot available
KeywordsIntersectionalityCategorical variablePhysical activitySociologyGender studiesPsychologyMedicineComputer sciencePhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Leisure-based physical activity is socially stratified in Canada. To date, inequalities in physical activity by race or ethnicity, gender, class or sexual orientation, in Canada and elsewhere, have largely been investigated as distinct, additive phenomena. Informed by intersectionality theory, this paper examines whether racial identity, gender, class and sexuality 'intersect' with one another to predict physical activity in data from Cycles 2.1 and 3.1 of the Canadian Community Health Survey (n= 149 574). In particular, we apply the intersectional principle of multiplicativity which suggests that people's experiences of their gender identities are raced, classed and sexualized; their racial experiences are gendered, classed and sexualized, and so forth. We find that the positive effect of income on being physically active is strong among visible minority men, of moderate strength among White men and women and weak to non-existent among visible minority women. We also find that a lesbian, gay or bisexual orientation corresponds with a higher likelihood of being physically active among women (especially among less educated women) but not among men. These multiplicative findings undermine additive approaches to investigating social inequalities in leisure-based physical activity and pave the way for future intersectional analyses of axes of inequality and their diverse, intersecting effects.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.063
GPT teacher head0.370
Teacher spread0.307 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations59
Published2016
Admission routes2
Has abstractyes

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