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Record W2913857385

Opportunity, Constraint, and Capital in Canadian Ice Hockey

2018· article· en· W2913857385 on OpenAlexaboutno aff
Andrew English

Bibliographic record

VenueScholarship@Western (Western University) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsIce hockeyConstraint (computer-aided design)MeteorologyEconomicsGeologyMathematicsGeographyGeometry
DOInot available

Abstract

fetched live from OpenAlex

The study explored the potential social benefits and motivations of participation in sport beyond those that pertain solely to healthy active living. Using organized youth ice hockey in Canada, the study also examined how cultural context can be a factor in the facilitation of such benefit, as well as the ways the game can reflect and reproduce inequalities in society more generally. A framework based on Pierre Bourdieu’s assertions on social, cultural, economic, and symbolic capital, was used to analyze data from ten semi-structured interviews with men who are former youth hockey players. The interview data revealed that through hockey – as a heteronormative institution – boys and men forge rich and extensive social networks, as well as acquire the linguistic skills, attitudes, and dispositions that help them to bond in a private and exclusive manner. Furthermore, their participation in an activity increasingly limited to the middle-to-upper class allows them to be socialized among this demographic, thus making economic capital – through job opportunities, for example – highly extractable from their social and cultural capital. To a certain extent, participants also derived a sense of connectedness to a Canadian national identity due to their participation in an activity that proliferates in their national context. Nevertheless, the study identifies a number of opportunities and constraints that create disparities in participation, and as such, the allocation of benefits. Since these inequalities in sport may help to reproduce inequalities in society at large, the study closes with recommendations for policy, programs, and future research.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.456

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0160.008
Scholarly communication0.0070.002
Open science0.0020.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.117
GPT teacher head0.334
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2018
Admission routes1
Has abstractyes

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