MétaCan
Menu
Back to cohort
Record W4312200729 · doi:10.1080/14927713.2022.2160787

Bridging brains: exploring neurosexism and gendered stereotypes in a mindsport

2022· article· en· W4312200729 on OpenAlexvenueno aff
Samantha Punch, Miriam Snellgrove, Elizabeth Graham, Charlotte McPherson, Jessica Cleary

Bibliographic record

VenueLeisure/Loisir · 2022
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsEmotiveEliteInequalityGender studiesBridge (graph theory)SociologyBridging (networking)Context (archaeology)Inclusion (mineral)PsychologySocial psychologyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Ingrained gendered discourses about women’s abilities and skills impact on their participation in leisure and sport. This paper argues that gendered stereotyping extends to the serious leisure context of mindsport in the form of neurosexism. The card game bridge is played by a roughly equal proportion of men and women but at elite-level male players significantly outperform female players worldwide. Based on 52 semi-structured interviews, the paper explores the everyday gendered assumptions that exist and are reproduced by elite bridge players. Many of the research participants draw on ideas of male brains being more rational, logical and competitive whereas women’s brains are perceived to be more emotive, unfocused and uncompetitive. These gendered stereotypes are used to explain and defend why more women are not playing at elite level. Such neurosexist and behaviourist assumptions actively reproduce inequality within mindsport to the detriment of women bridge players. This article shows that neurosexism reinforces ongoing, systemic inequalities around gendered experiences of serious leisure, thereby reproducing gendered inequalities and hindering greater participation and inclusion in mindsport.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.999

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 integrity0.0000.001
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 head0.084
GPT teacher head0.314
Teacher spread0.231 · 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.

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

Citations6
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueLeisure/LoisirSame topicSport Psychology and PerformanceFrench-language works237,207