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Record W2517192924 · doi:10.1177/1069397116665815

Sex Differences in Sport Remain When Accounting for Countries’ Gender Inequality

2016· article· en· W2517192924 on OpenAlexafffund
Shea M. Balish, Robert O. Deaner, Daniel Rainham, Chris M. Blanchard

Bibliographic record

VenueCross-Cultural Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsDalhousie University
FundersCanada Research ChairsDalhousie UniversityUniversity of Ottawa
KeywordsInequalityTest (biology)Social inequalityWorld Values SurveyGender inequalityEconomic inequalityMultilevel modelSocial psychologyPsychology

Abstract

fetched live from OpenAlex

The spectator lek hypothesis argues that sex differences in preferences for sport largely stem from evolved predispositions and thus should be universal or near universal, whereas socioconstructivist hypotheses argue that such sex differences are entirely socially constructed and thus should vary as a function of a society’s gender inequality. To test these competing hypotheses, cross-national nested data were acquired from the International Social Survey Program (s s = 49,729, n countries = 34). Hierarchical linear modeling was used to examine whether sex differences in sport are universal or near universal when controlling for countries’ gender inequality. Findings indicate that even when controlling for gender inequality, sex differences remain in reporting sport as one’s most common activity, in watching sport, and in attending sport events and for agreeing with the statement that one plays sport to compete against others. Although this study was limited by the homogeneity of the sampled countries and the use of self-report measures, these findings nonetheless support the spectator lek hypothesis. Future research should examine case studies (e.g., matrilineal societies) that can specifically test the assumptions of the spectator lek hypothesis.

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)0.008
metaresearch head score (Gemma)0.004
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 score0.474
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.287
GPT teacher head0.514
Teacher spread0.227 · 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

Citations23
Published2016
Admission routes2
Has abstractyes

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