MétaCan
Menu
Back to cohort
Record W2477557809 · doi:10.1080/2159676x.2016.1212915

Football as a terrain of hope and struggle: beginning a dialogue on social change, hope and building a better world through sport

2016· article· en· W2477557809 on OpenAlexafffund
Shawn Forde, Ayanda Kota

Bibliographic record

VenueQualitative Research in Sport Exercise and Health · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGrassrootsFootballSocial changeSociologyObject (grammar)Political scienceSociology of sportPublic relationsPoliticsGender studiesLaw

Abstract

fetched live from OpenAlex

From diplomats and politicians, to the executives of sports governing bodies and non-governmental development agencies, to grassroots activists, sport – particularly football – is invariably invoked as an object of hope and a vehicle for building a better world. However, how hope is conceived by these various actors and institutions, and the better world that they imagine is often left unexamined. The purpose of this paper, a collaboration between a researcher interested in sport for development and peace and an activist involved in South African social movements and community football, is to begin an exploration of different understandings of hope and social change in relation to sport. Our aim is to demonstrate that reorienting discussions around how social change is conceptualised in different ways can result in critical understandings of how sport is currently being mobilised in various ways to affect change, and the potential alternatives that are being overlooked.

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)0.017
metaresearch head score (Gemma)0.012
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0260.087
Scholarly communication0.0180.022
Open science0.0020.016
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0050.000

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 head0.376
GPT teacher head0.565
Teacher spread0.189 · 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

Citations12
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueQualitative Research in Sport Exercise and HealthSame topicSport and Mega-Event ImpactsFrench-language works237,207