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Record W4399548919 · doi:10.1177/01937235241254116

Listen, Tell, Show: Recreation and the Black and Decolonial Storytelling in Sport and Physical Culture Research

2024· article· en· W4399548919 on OpenAlexaff
Janelle Joseph

Bibliographic record

VenueJournal of Sport and Social Issues · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRecreationStorytellingSociologyPhysical cultureGender studiesArtPolitical scienceNarrativeLiteratureMedicine

Abstract

fetched live from OpenAlex

Stories are always grounded in personal, collective, and ancestral experiences and come in a variety of visual, performance, textile, and text-based forms. Physical culture participation and politics can be better understood by engaging with the stories of people facing ongoing colonial and epistemic injustices, intersectional oppressions, as well as structural and cultural racism. In contrast to dominant trends in the cultural politics of sport, this article offers an ontological, epistemological, and methodological alternative. Following Black and decolonial scholars, we must honor non-Western storytelling modalities to listen to, tell, show, and center many experiences to resist Western colonial culture's binary structures and hierarchies, introduce alternate theorizations, and inject other ways of knowing and being into the sport and physical cultures and into physical cultural studies. This article highlights several areas of Black and decolonial studies that will be essential to efforts at transformative justice in sport and sports scholarship: interdisciplinarity, the bios–mythoi relationship, counter-storytelling, and creation stories.

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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.056
GPT teacher head0.412
Teacher spread0.356 · 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 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

Citations15
Published2024
Admission routes1
Has abstractyes

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