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Record W2166884278

TGFU AND ITS GOVERNANCE: FROM CONCEPTION TO SPECIAL INTEREST GROUP

2015· article· es· W2166884278 on OpenAlexaff
Joy Butler, Alan Ovens

Bibliographic record

VenueUVaDOC UVaDOC University of Valladolid Documentary Repository (University of Valladolid) · 2015
Typearticle
Languagees
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNarrativePedagogyPhysical educationHumanitiesSet (abstract data type)SociologyPsychologyArtLiterature
DOInot available

Abstract

fetched live from OpenAlex

Teaching Games for Understanding (TGfU) has emerged over the past thirty years as one of the leading instructional models for sports coaches and physical education teachers. From its initial beginning as a set of theoretical and practical initiatives on how to teach games, TGfU has evolved to become one of the most readily identifiable pedagogical movements within the sports and physical education field. In this paper we aim to document and study this development in a way that acknowledges the complexity and collectivity involved. At one level, it is easy to see that there is a broad mix of people who value this model and want to work in a collaborative way to promote, research and advance it. At another level, however, the problem becomes one of resisting the urge to simply tell the history without acknowledging the methodological issues involved. As historians would remind us, it is important that we never take history as fixed and linear. Instead, we must interrogate the popular construction of history and seek alternative perspectives in order to escape the confines of biography and experience. By reflecting on the dominant narratives, as well as a few counter narratives, we have a means to engage with and understand how key pedagogical initiatives, like TGfU, are supported and sustained in educational contexts.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.060
GPT teacher head0.319
Teacher spread0.259 · 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.

Study designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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