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Record W2785342939 · doi:10.21083/nrsc.v0i11.4002

Encourager les réflexions/interactions affectives par l’usage scénarisé des outils du Web 2.0 : en quoi est-ce significatif ?

2018· article· fr· W2785342939 on OpenAlexaffvenueabout
Sarah Anthony, Prisca Fenoglio

Bibliographic record

VenueNouvelle Revue Synergies Canada · 2018
Typearticle
Languagefr
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsMcGill University
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Dans le contexte universitaire de l’apprentissage/enseignement du français langue seconde au Québec, à travers l’analyse d’un scénario pédagogique amenant l’apprenant à (re)découvrir de manière introspective et affective les lieux montréalais qui lui sont chers pour aboutir à une vidéo finale créée et partagée au moyen des outils du Web 2.0, nous explorons la dyade affects/apprentissages et l’effet du Web social sur celle-ci.Nous cherchons à savoir si une réflexion/interaction affective avec soi-même, avec autrui ou avec le lieu/la culture francophone, soutenue par la scénarisation pédagogique et l’usage du Web social, peut être significative dans le cadre de l’apprentissage, c’est-à-dire motivante et/ou en lien au développement de savoir-être, de savoir-faire et de savoirs.Nous montrons que l’expérience est appréciée et vue comme marquante, davantage que comme motivante, par les apprenants et qu’elle favorise l’acquisition de savoir-être et de savoir-faire créant un terrain propice à l’apprentissage.

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)0.003
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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
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".

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Citations1
Published2018
Admission routes3
Has abstractyes

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Same venueNouvelle Revue Synergies CanadaSame topicEFL/ESL Teaching and LearningFrench-language works237,207