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Record W3044365844 · doi:10.55765/atps.i17.643

Partage de savoirs en développement social municipal

2020· article· fr· W3044365844 on OpenAlexaboutno aff
Claude Champagne

Bibliographic record

VenueRevue internationale animation territoires et pratiques socioculturelles · 2020
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesSocial innovationSociologyArt

Abstract

fetched live from OpenAlex

Les communautés de pratique (CP) sont maintenant mises en place dans une diversité de lieux de travail pour favoriser l’apprentissage et le partage de savoirs. L’auteur pose un regard réflexif sur son travail d’animateur et d’accompagnateur depuis douze ans du Forum des intervenants municipaux en développement social (FIMDS) à la Ville de Montréal. Cette CP multidisciplinaire répond au besoin de développer des compétences collectives pour mieux assumer les responsabilités dévolues aux villes en matière de développement social. Dressant un bilan critique des stratégies pédagogiques utilisées, l’auteur cible des conditions favorisant les apprentissages et un plus grand transfert de savoirs et d’expertises dans les milieux visés. Il se demande enfin comment exercer plus d’influence et assurer une pérennité, même si des enjeux de pouvoir entre des élus, des gestionnaires et des intervenants (incluant l’animateur) peuvent parfois freiner le développement d’une culture d’apprentissage et d’innovation.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.353
Teacher spread0.286 · 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
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
Published2020
Admission routes1
Has abstractyes

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