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Record W2230659019 · doi:10.7202/1033742ar

Travail de rue, reconnaissance et citoyenneté : étude d’un cas montréalais1

2015· article· fr· W2230659019 on OpenAlexaffvenueabout
Eduardo Castillo-González, Élodie Marion, Mélody Saulnier

Bibliographic record

VenueService social · 2015
Typearticle
Languagefr
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsÉcole Nationale d'Administration Publique
Fundersnot available
KeywordsHumanitiesPolitical scienceSociologyArt

Abstract

fetched live from OpenAlex

Cet article présente l’étude d’un programme de Travail de rue s’adressant aux jeunes en difficultés dans un arrondissement de la ville de Montréal. Le document vise essentiellement la discussion des effets du programme sur la population desservie. La combinaison de méthodes qualitatives et quantitatives a permis de récolter un riche matériau de recherche de même que d’identifier deux sphères de la vie sociale des jeunes sur lesquelles le travail de rue, une pratique de proximité, semble avoir un effet important : le développement d’un sentiment de reconnaissance et l’exercice de la citoyenneté. Qui plus est, les analyses permettent d’entrevoir dans ce milieu l’impact positif de la pratique du travail de rue, cet impact étant davantage perceptible chez les filles que chez les garçons. Enfin, les particularités du travail de rue en tant que pratique d’intervention, son caractère atypique et les stratégies d’intervention qui y sont reliées sont discutées à différents moment dans cet article.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.124
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0080.004
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.075
GPT teacher head0.388
Teacher spread0.314 · 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

Citations2
Published2015
Admission routes3
Has abstractyes

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