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Record W1516129447 · doi:10.4324/9780203846896

Social Capital and Sport Governance in Europe

2010· book· en· W1516129447 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceSocial capitalPolitical scienceEconomic systemBusinessEconomicsFinanceLaw

Abstract

fetched live from OpenAlex

1. Social Capital, Governance and Sport Barrie Houlihan and Margaret Groeneveld 2. The EU and Sport Governance: Between Economic and Social Values Borja Garcia 3. Czech Sport Governance Cultures and a Plurality of Social Capitals: Politicking Zone, Movement and Community Dino Numerato 4. Danish Sport Governance: Tradition in Transition H. Thomas R. Persson 5. Sport and Social Capital in England Andrew Adams 6. Social Capital and Sport Governance in France Cristina Fusetti 7. Sport, Divided Societies and Social Capital in Ireland David Hassan and Allan Edwards 8. The Social Capital of Sport: The Case of Italy Simone Baglioni 9. Governance and Social Capital: Democratic Effects and Policy Outcomes in a Nordic Sport Model Ornulf Seippel 10. Conclusion: Understanding Social Capital as Both Metaphor and Traditional Form of Social Exchange Margaret Groeneveld and Fabien Ohl

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.005
Scholarly communication0.0050.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.289
Teacher spread0.271 · 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 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

Citations42
Published2010
Admission routes1
Has abstractyes

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