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Record W2610681649

Transformations through sport : the case of capoeira and basketball

2017· article· en· W2610681649 on OpenAlexaff
Mike Baynham, Jessica Bradley, John Callaghan, Jitka Hanušová, Erik Moore, J. Simpson

Bibliographic record

VenueWhite Rose Research Online (University of Leeds, The University of Sheffield, University of York) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMartial Arts: Techniques, Psychology, and Education
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsBasketballSociologyPoliticsIdeologySection (typography)Media studiesRelation (database)Political scienceAdvertisingHistory
DOInot available

Abstract

fetched live from OpenAlex

The third phase of the AHRC-funded Translation and Translanguaging (TLang) project focuses on the theme of Sport. The Key Participant in Leeds is Tiago from Mozambique who is involved in capoeira and basketball, which gives our case study a dual focus. It should be noted that our analysis of both sets of sessions, capoeira and basketball, while kept roughly in parallel in the report, also reflects the different opportunities and affordances of the activities: capoeira for example provided notable opportunities for participants to learn Portuguese, while there was no such obvious equivalent in basketball. In Section One we introduce the case study, then in Section Two we introduce Tiago and look at the role that basketball and capoeira has played in his transformations and ideological becoming when he was growing up in Mozambique but also since he moved to England. We see how sport has always played a central shaping role in his life. Next in Section Three we introduce the two sports, basketball and capoeira (though as we shall see, capoeira, designated a UNESCO World Heritage treasure, is something more multi-layered than just a sport). In Section Four we review some of the themes that have cut across the TLang case studies so far: the work/home dynamic, the dynamics and politics of space, including borrowed space, entrepreneurship and precarity (finding a place and transforming what one knows, is and can do into something marketable), and finally of course a reflection on sport in relation to the core themes of our project, translanguaging, mobility, globalization and superdiversity. We show how Tiago, caught in the trap of precarious hourly paid work, is striving to transform an activity he loves, capoeira, into something he could earn a living by. The challenge of the Sports case study methodologically lay in the fact that we were dealing with highly visual data which could only really be captured on video. Additionally, due to difficulties in sound recording we were further led to consider the dynamic interaction of visual, verbal and embodied action rather than extensive analysis of spoken data. In Section Five we therefore focus on methodological issues concerned with obtaining and working with such visual data. In the first part of Section Six, we look at the event structure of both capoeira and basketball sessions, then go on to provide more detailed analysis of video data. In the case of capoeira we focus on the roda stage, which is the culmination of each session; in the case of basketball, we look at the lead up to an actual game: warm up, practice and strategy setting. In the final part of this section we look at the language learning opportunities afforded by participation in the capoeira group, and interaction both in English and Brazilian Portuguese with a marked Afro-Brazilian inflection. This is both through the songs and chants that are characteristic of capoeira, but also the language of instruction and regulation of the activity, where Portuguese/English translanguaging is often in evidence. In Section Seven we briefly conclude the case study.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.131
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0420.028
Scholarly communication0.0110.005
Open science0.0030.010
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0090.001

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 head0.091
GPT teacher head0.357
Teacher spread0.266 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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