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Record W4378373232 · doi:10.1080/01490400.2023.2217161

Bicycles and the Potential of Unstructured Sport for Development and Peace

2023· article· en· W4378373232 on OpenAlexafffund
Mitchell McSweeney, Lyndsay Hayhurst, Brad Millington, Brian Wilson, Janet Otte, Lidieth del Socorro Cruz Centeno, Madison Ardizzi, Emerald Bandoles

Bibliographic record

VenueLeisure Sciences · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of British ColumbiaBrock UniversityYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTransformational leadershipSociologyPublic relationsPsychologyPolitical science

Abstract

fetched live from OpenAlex

Sport for Development and Peace (SDP) continues to grow, both in research and practice. Recently, researchers have considered the merits of unstructured sport as a way of realizing sport’s benefits while circumnavigating its perceived problematic elements. This article reports findings from research on ‘Bicycles for Development’ (BFD) – a movement that trades on bicycle access as a way of achieving development objectives. We draw from interviews and fieldwork with BFD stakeholders, with the aim of examining BFD in relation to the SDP field – and de-sportization especially. Three research findings are relevant along these lines: (1) bicycles as beneficial due to their inherent multi-functionality; (2) the merits of unstructured physical activity; and (3) factors that impact negatively on bicycle access and thus might hinder BFD’s transformational potential. We conclude with reflections on BFD’s potential in subverting both common notions of sport in SDP and the neoliberal model of SDP provision.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.034
GPT teacher head0.326
Teacher spread0.292 · 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 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

Citations5
Published2023
Admission routes2
Has abstractyes

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