Transformations through sport : the case of capoeira and basketball
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,042 | 0,028 |
| Communication savante | 0,011 | 0,005 |
| Science ouverte | 0,003 | 0,010 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».