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Record W2008191584 · doi:10.3917/cris.2179.0005

Les fédérations sportives

2013· article· fr· W2008191584 on OpenAlexaff
Thierry Zintz, Mathieu Winand

Bibliographic record

VenueCourrier hebdomadaire du CRISP · 2013
Typearticle
Languagefr
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsMinistère des Ressources naturelles et des ForêtsWorld Anti-Doping Agency
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

En Belgique, les fédérations sportives sont des acteurs essentiels dans le domaine du sport et de l’activité physique. Au nombre de cent soixante environ, elles regroupent plusieurs centaines de clubs, qui affilient quelque deux millions de personnes. La présente étude est consacrée au cadre politique, juridique, économique et social dans lequel évoluent les fédérations belges. Au niveau mondial, diverses structures réglementent la pratique sportive en promouvant l’exercice physique, en harmonisant les réglementations, en organisant les compétitions internationales ou en réglant les litiges (UNESCO, OMS, CIO, Tribunal arbitral du sport, Agence mondiale antidopage, fédérations internationales, etc.). Au niveau européen, l’attention des autorités se porte sur les apports sociaux et éducationnels du sport, ainsi que sur ses implications économiques. Au niveau belge, enfin, une multitude d’instances encadrent les activités des fédérations sportives : l’Autorité fédérale, les communautés, les régions, les provinces, les communes, le COIB, l’ADEPS, le BLOSO, etc. Situant le rôle et l’influence de chacun de ces acteurs, ce Courrier hebdomadaire analyse notamment la gestion du sport dans l’architecture institutionnelle, particulièrement complexe, de la Région bruxelloise. Dans une visée plus prospective, T. Zintz et M. Winand abordent également la question de la performance organisationnelle du système sportif belge.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.180
Threshold uncertainty score0.358

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0060.002
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0420.010

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 head0.032
GPT teacher head0.310
Teacher spread0.278 · 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

Citations9
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueCourrier hebdomadaire du CRISPSame topicSport and Mega-Event ImpactsFrench-language works237,207