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Se mouvoir par-delà les frontières au moyen d’un projet bénévole

2016· article· fr· W2603019119 on OpenAlexaffabout
Marie‐Claude Plourde, Consuelo Vásquez, Sophie Del Fa

Bibliographic record

VenueQuestions de communication · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceESPACEPhilosophy

Abstract

fetched live from OpenAlex

L’article repose sur une étude ethnographique menée en collaboration avec la Société canadienne du cancer sur l’un de ses programmes de prévention, le réseau d’autobus pédestres nommé Trottibus. À partir d’une réflexion portant sur l’organisation bénévole qui s’inscrit dans une approche constitutive de la communication organisationnelle, nous montrons les effets du mouvement de ce projet de bénévolat sur le plan social en questionnant comment, en tant que mode particulier d’organisation, celui-ci déplace les frontières dans un quartier. Pour ce faire, nous explorons empiriquement : (1) comment le Trottibus permet de traverser les frontières entre les générations par le renforcement des liens bénévoles-enfants par la nature même de l’activité qu’il promeut : le transport actif ; (2) en quoi ce mouvement à travers les frontières favorise le sentiment d’appartenance au quartier et génère l’autonomie des enfants dans leur réappropriation de l’espace urbain ; et, par conséquent, (3) dans quelle(s) mesure(s) le Trottibus floute les frontières de l’École par son déplacement de l’extérieur vers l’intérieur (et vice versa). De l’intégration de ce projet bénévole dans une collectivité scolaire, nous concluons sur le renouvellement du rôle de l’École en tant qu’espace rassembleur et générateur d’une vie communautaire.

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)0.007
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: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0050.004
Open science0.0010.005
Research integrity0.0010.003
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 head0.198
GPT teacher head0.442
Teacher spread0.245 · 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
Published2016
Admission routes2
Has abstractyes

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