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Record W2617611687

L'échange de connaissances en petite enfance: Comment mettre à profit les expertises des chercheurs et des praticiens

2011· book· fr· W2617611687 on OpenAlexaboutno aff
Nathalie Bigras, Caroline Bouchard

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2011
Typebook
Languagefr
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt
DOInot available

Abstract

fetched live from OpenAlex

Rapprocher les chercheurs et les praticiens de la petite enfance par un partage interactif de leurs savoirs, voila a quoi correspond le principe d’echange de connaissances. Bien qu’un ecart considerable soit reconnu entre les connaissances obtenues par la recherche et celles utilisees dans la pratique, il semble que plusieurs experiences recentes aient ete amorcees au Quebec afin de rapprocher les chercheurs et les praticiens issus de divers contextes de vie (services de garde, ecoles, milieux communautaires, services sociaux). De surcroit, et bien que traditionnellement mises sur pied par les chercheurs, plusieurs de ces experiences d’echange de connaissances l’ont ete par des praticiens. Quelles sont ces activites d’echange de connaissances menees dans le domaine de la petite enfance au Quebec ? Comment ces experiences ont-elles vu le jour et par la suite pris forme ? Quels sont les cadres de reference sur lesquels s’appuient ces experiences ? Quels en sont les caracteristiques, les obstacles et les gages de succes ? Quels types d’activites d’echanges sont menes ? Ces questions sont au cœur de ce collectif qui reunit des chercheurs et des praticiens preoccupes par l’echange de connaissances, que l’on parle de projets de grande envergure mobilisateurs et rassembleurs, de projets lies a l’evaluation et a l’intervention en petite enfance, de projets locaux visant la reussite scolaire ou de projets visant l’accroissement des connaissances sur les pratiques educatives et la qualite des service

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)0.005
metaresearch head score (Gemma)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.006
Science and technology studies0.0010.003
Scholarly communication0.0000.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.298
GPT teacher head0.428
Teacher spread0.130 · 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 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

Citations2
Published2011
Admission routes1
Has abstractyes

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