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Record W1485211214 · doi:10.7202/010243ar

Étudier, ou travailler, pour apprendre : travailler à l’école et apprendre en emploi?

2004· article· fr· W1485211214 on OpenAlexvenueaboutno aff
Andrée G. Roberge

Bibliographic record

VenueCahiers québécois de démographie · 2004
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceHabilitationArt

Abstract

fetched live from OpenAlex

Cet article souligne l'importance accrue de l'intrusion de l'univers du travail dans la vie et la formation des élèves de secondaire et des collégiens québécois. L'auteure décrit d'abord l'ampleur récente du travail rémunéré des jeunes pendant l'année scolaire, puis expose les motifs et les incidences du phénomène. Cette insertion d'activités professionnelles dans la vie des étudiants, qui apparaît au départ comme une initiative individuelle, se transforme de plus en plus, avec de multiples variantes, en formule privilégiée de formation en partenariat avec l'entreprise. L'auteure s'interroge enfin sur la transformation tacite des objectifs d'une formation qui, hier encore, était perçue comme un processus d'habilitation des futurs citoyens, et non pas seulement de qualification des travailleurs.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.643

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0200.016
Scholarly communication0.0080.004
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.017
GPT teacher head0.277
Teacher spread0.259 · 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 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
Published2004
Admission routes2
Has abstractyes

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