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Record W4306803005 · doi:10.3917/agora.092.0097

Les jeunes dans les politiques des conseils régionaux : cartographie d’une prise en compte entre secteurs, population et territoire

2022· article· fr· W4306803005 on OpenAlexaff
Jordan Parisse, Maëlle Moalic

Bibliographic record

VenueAgora débats/jeunesses · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsCentre Jeunesse de Quebec
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Cet article explore les modalités selon lesquelles les conseils régionaux, en France hexagonale, mettent en œuvre des politiques en direction des jeunes et inscrivent cette catégorie populationnelle comme enjeu de gouvernance. Il analyse, tout d’abord, l’évolution du champ de compétence des conseils régionaux dans la prise en compte des jeunes, à travers l’observation de différents dispositifs leur étant spécifiquement dédiés et l’étude des politiques menées dans les domaines de l’éducation et de l’orientation. Il montre, ensuite, comment les conseils régionaux s’approprient de manière différenciée le rôle de collectivité « cheffe de file » en matière de « politique de la jeunesse » que leur confère la loi depuis 2017, tout en soulignant les formes de mise à distance des jeunes dans ces processus.

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), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0040.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.331
Teacher spread0.286 · 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 designObservational
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

Citations0
Published2022
Admission routes1
Has abstractyes

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