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Record W2904377596 · doi:10.4000/echogeo.16209

Les territoires du football en Tunisie (1906-2015)

2018· article· fr· W2904377596 on OpenAlexaff
Ali Langar, Myriam Baron, Claude Grasland

Bibliographic record

VenueEchoGéo · 2018
Typearticle
Languagefr
FieldSocial Sciences
TopicMulticulturalism, Politics, Migration, Gender
Canadian institutionsCanadian Cartographic Association
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

En préparation de la coupe du monde de football en Russie aux mois de juin et juillet 2018, un article publié dans le journal Le Monde au mois de mars soulignait que les Tunisiens semblaient moins attachés à leur équipe nationale qu’aux principaux clubs évoluant dans le championnat de première division. Ce constat est à mettre en regard avec la manière dont ce sport s’est implanté et diffusé durant le protectorat français, le rôle politique qu’il a pu jouer que ce soit dans la marche vers l’indépendance ou, plus récemment, lors des grandes manifestations de la révolution de jasmin durant l’hiver 2010-2011. Enfin, le football, premier sport en Tunisie par le nombre de licenciés, s’adosse mais aussi contribue en partie à entretenir les grandes inégalités territoriales entre le littoral et l’intérieur, le Nord et le Sud par l’implantation des principales équipes participant au championnat le plus prestigieux, celui de la Ligue ou Division 1. Toutefois, quand sont prises en compte les Ligues régionales, force est de constater que ce sport joue aussi un rôle notable dans les manières dont l’aménagement et les rééquilibrages au sein du territoire national peuvent être appréhendés.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.815

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 studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.130
GPT teacher head0.414
Teacher spread0.285 · 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 designQualitative
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

Citations3
Published2018
Admission routes1
Has abstractyes

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