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Record W2774431099

Approches dominantes du décrochage scolaire : un regard critique des écrits récents au Québec et ailleurs

2017· article· fr· W2774431099 on OpenAlexaboutno aff
Jeremias Demba, J. Vincent H. Morrissette

Bibliographic record

VenueJournal de la Recherche Scientifique de l'Universite de Lome · 2017
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceSociologyArt
DOInot available

Abstract

fetched live from OpenAlex

Dans des societes ou la reussite sociale depend de plus en plus fortement du niveau scolaire, du diplome obtenu, le decrochage scolaire devient une question preoccupante. L’abandon des etudes ou la non-obtention d’un diplome peut conduire au chomage, a l’exclusion sociale. Eleves, parents, enseignants, administrateurs scolaires, chercheurs, etc., sont donc interpeles par l’ampleur des repercussions de ce phenomene sur le cheminement scolaire de l’eleve ainsi que sur son eventuelle insertion socioprofessionnelle. Bien plus, la persistance des inegalites - de genre notamment - suscite une attention particuliere, notamment chez les chercheurs. Cette recension des ecrits vise a rendre compte des approches dominantes sur la question au Quebec et ailleurs. Si celles-ci presentent l’interet d’etablir des relations entre ces differences de carriere scolaire et les determinismes personnels, familiaux, sociaux et scolaires, contribuant ainsi, a la reponse politique et institutionnelle face au decrochage scolaire, il convient tout de meme de poser sur elles un regard critique et de proposer quelques prospectives en recherche. Mots-cles : Decrochage scolaire, abandon scolaire, determinismes personnels, familiaux, sociaux et scolaires, Quebec.

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)0.003
metaresearch head score (Gemma)0.004
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: Review · Consensus signal: none
Teacher disagreement score0.088
Threshold uncertainty score0.637

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0190.022
Scholarly communication0.0100.004
Open science0.0020.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0090.000

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 head0.300
GPT teacher head0.461
Teacher spread0.162 · 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
GenreReview

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
Published2017
Admission routes1
Has abstractyes

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