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Record W616209678 · doi:10.4324/9780203408773

Sport Governance

2013· book· en· W616209678 on OpenAlexaboutno aff
Ian O’Boyle, Trish Bradbury

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsGeorge (robot)Corporate governanceEnvironmental ethicsHistoryHumanitiesPolitical scienceClassicsManagementArt historyArtPhilosophy

Abstract

fetched live from OpenAlex

Governance has become a hugely important issue within sport. Issues of corruption and ‘bad governance’ have become synonymous with some aspects of sport and closer scrutiny than ever before is being applied to ensure organisations are following international best practice in respect to how they are governed. As sport organisations are required to become more professional and to adopt a more transparent and accountable approach to their operations, it has become important for all students, researchers and professionals working in sport to understand what good governance is and how it should be achieved. This book is the first to examine sport governance around the world. It offers a series of in-depth case studies of governance policy and practice in 15 countries and regions, including the US, UK, China, Australia, Canada, South Africa, Latin America and the Middle East, as well as chapters covering governance by, and of, global sport organisations and international sport federations. With an introduction outlining the key contemporary themes in the study of sport governance, and a conclusion pointing at future directions for research and practice, this book is essential reading for any course on sport management, sport policy, sport development, sport administration or sport organisations, and for any manager or policy-maker working in sport and looking to improve their professional practice.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.465
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.004

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 head0.022
GPT teacher head0.287
Teacher spread0.265 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations26
Published2013
Admission routes1
Has abstractyes

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