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Record W2142765960 · doi:10.51657/ric.v3i1.41024

L’apprentissage expansif et la construction de l’identité de jeunes à travers la réalisation d’un documentaire scientifique : un projet d’agentivité transformatrice

2015· article· fr· W2142765960 on OpenAlexaffvenue
Jrène Rahm, Émilie Boulanger, Issac Hébert, Gwénaëlle Journet, Audrey Lachaîne

Bibliographic record

VenueRevue internationale du CRIRES innover dans la tradition de Vygotsky · 2015
Typearticle
Languagefr
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsCegep Edouard MontpetitMinistère des Ressources naturelles et des Forêts (Québec)Université de Montréal
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Inspirs par les travaux de Vygotsky, nous examinons l'apprentissage selon une vision de l'individu comme crateur et agent de changement. En nous appuyant sur ses lments cls de la thorie socioculturelle, nous entendons explorer ce qu'un tel concept, jumel celui de construction de l'identit, peut sous-tendre dans le cadre du fonctionnement d'un club scientifique parascolaire mis sur pied collectivement, et proposant des activits destines des jeunes d'une cole secondaire. Nous nous intressons plus particulirement au processus de ralisation en commun d'un documentaire vido portant sur les sciences, processus qui s'est tal sur douze semaines. Nous entendons ainsi explorer des formes d'implication en sciences qui ne correspondent pas aux modles normatifs typiques des sciences enseignes l'cole. Nous voulons du coup montrer dans quelle mesure une telle implication favorise l'apprentissage cratif et la construction de l'identit en matire de sciences ainsi que l'mergence d'une communaut d'apprentissage propice autant l'agentivit transformatrice des jeunes qu' celle des animateurs.

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)0.010
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0160.025
Scholarly communication0.0220.011
Open science0.0010.013
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0100.001

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 head0.047
GPT teacher head0.337
Teacher spread0.290 · 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 designQualitative
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

Citations1
Published2015
Admission routes2
Has abstractyes

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