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Record W2322787895 · doi:10.1123/ssj.2014-0060

Feminist Cultural Studies: Uncertainties and Possibilities

2015· article· en· W2322787895 on OpenAlexaff
Mary Louise Adams, Michelle T. Helstein, Kyoung-yim Kim, Mary G. McDonald, Judy E. Davidson, Katherine M. Jamieson, Samantha King, Geneviève Rail

Bibliographic record

VenueSociology of Sport Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of AlbertaUniversity of LethbridgeQueen's University
Fundersnot available
KeywordsScholarshipAppropriationFeminismCultural studiesSociologyGender studiesField (mathematics)Media studiesFeminist theoryPolitical scienceEpistemologyAnthropologyLaw

Abstract

fetched live from OpenAlex

This collection of commentaries emerged from ongoing conversations among the contributors about our varied understandings of and desires for the sport studies field. One of our initial concerns was with the absence/presence of feminist thought within sport studies. Despite a rich history of feminist scholarship in sport studies, we have questioned the extent to which feminism is currently being engaged or acknowledged as having shaped the field. Our concerns crystallized during the spirited feminist responses to a fiery roundtable debate on Physical Cultural Studies (PCS) at the annual conference of the North American Society for the Sociology of Sport (NASSS) in New Orleans in November 2012. At that session, one audience member after another spoke to what they saw as the unacknowledged appropriation by PCS proponents of longstanding feminist—and feminist cultural studies—approaches to scholarship and writing. These critiques focused not just on the intellectual moves that PCS scholars claim to be making but on how they are made, with several audience members and some panelists expressing their concerns about the territorializing effects of some strains of PCS discourse.

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)0.065
metaresearch head score (Gemma)0.073
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.065
Threshold uncertainty score0.344

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0650.073
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0290.076
Scholarly communication0.0220.024
Open science0.0050.014
Research integrity0.0110.016
Insufficient payload (model declined to judge)0.0060.001

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 head0.111
GPT teacher head0.389
Teacher spread0.278 · 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 designTheoretical or conceptual
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

Citations38
Published2015
Admission routes1
Has abstractyes

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