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Record W4399820761 · doi:10.1515/9782760549425

Instruire, corriger, guérir?

2018· book· fr· W4399820761 on OpenAlexaboutno aff
Julien Prud’homme

Bibliographic record

VenuePresses de l'Université du Québec eBooks · 2018
Typebook
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Au Québec, le nombre d’élèves en difficulté est passé de 20 000 en 1964 à 200 000 en 2016. Les diagnostics se sont multipliés, créant une école complexe, peuplée d’une myriade de nouveaux métiers cliniques ou pédagogiques. Que penser de ces mutations ? Faut-il y voir un progrès, un risque de médicalisation abusive, ou les deux à la fois ? Comment les décideurs, le public et les professionnels peuvent-ils s’y retrouver ? Un retour s’impose sur les origines et sur les mécanismes – professionnels, politiques et scientifiques – qui façonnent cette ­évolution de l’école québécoise. Le présent ouvrage raconte l’histoire des orthopédagogues du Québec. Ce corps de métier largement féminin, né de la Révolution tranquille, consacré aux élèves en difficulté d’apprentissage, présente un parcours atypique et franchit aujourd’hui des étapes décisives sur le chemin de la reconnaissance professionnelle. L’histoire de l’orthopédagogie embrasse celle de la difficulté scolaire et de ses ambiguïtés. Elle s’écrit autant dans les salles de classe que dans les sphères politique et scientifique. En retraçant le parcours d’un corps de métier, cette histoire nous aide à comprendre le passé, le présent et l’avenir de l’école québécoise.

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)0.005
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.921
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0160.011
Scholarly communication0.0060.004
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0390.008

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 head0.077
GPT teacher head0.310
Teacher spread0.233 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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