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Record W4396756344 · doi:10.1515/9782760550827

Programmes de prévention et développement de l'enfant

2019· book· fr· W4396756344 on OpenAlexaboutno aff
George M. Tarabulsy, Julie Poissant, Thomas Saïas, Cécile Delawarde

Bibliographic record

VenuePresses de l'Université du Québec eBooks · 2019
Typebook
Languagefr
FieldHealth Professions
TopicHealth, Medicine and Society
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

Depuis les 50 dernières années, les pays occidentaux ont implanté, sous une forme ou une autre, des programmes visant à soutenir les familles vivant dans des contextes de vulnérabilité sociale et économique. Fondés sur la prémisse qu’il est important d’aider les familles à améliorer leur situation et à devenir plus autonomes et impliquées dans leur communauté, ces programmes encouragent le développement des enfants sur les plans social, émotionnel, cognitif et langagier. Dans tous ces programmes, le développement de l’enfant est l’enjeu prioritaire. Le présent ouvrage décrit la recherche fondamentale et appliquée réalisée depuis les années 1960 pour renforcer les chances des enfants grandissant dans des circonstances de vulnérabilité. Les auteurs, Américains, Européens, Canadiens et Québécois, sont à l’origine de travaux à la fois rigoureux et novateurs dans le domaine. Sincères dans leur souhait de favoriser l’égalité des chances, ils soulignent la promesse des programmes de prévention précoce, ainsi que les défis qu’ils posent à nos sociétés dans l’organisation des services offerts à l’enfance.

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)0.005
metaresearch head score (Gemma)0.008
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 score0.258
Threshold uncertainty score0.512

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.002

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 head0.025
GPT teacher head0.310
Teacher spread0.284 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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Same venuePresses de l'Université du Québec eBooksSame topicHealth, Medicine and SocietyFrench-language works237,207