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Record W4399449075 · doi:10.7202/1111615ar

La crise climatique transforme les pratiques en travail social : l’OTSTCFQ doit s’adapter

2024· article· fr· W4399449075 on OpenAlexaffabout
Marie-Hélène Gauthier, Lyanne Levasseur Faucher, Mikayla Salmon-Beitel, Elsa Vadnais-Malo

Bibliographic record

VenueIntervention · 2024
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsMinistère de l’Emploi et de la Solidarité Sociale (Québec)Université de Montréal
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

La justice environnementale, si elle s’enracine dans une optique de réconciliation avec les peuples autochtones, est essentielle pour aborder les problématiques écosociales actuelles. Les travailleuses sociales, en tant que professionnelles qui soutiennent les personnes les plus vulnérables, se trouvent dans une position privilégiée pour oeuvrer en faveur de la justice environnementale, mais sont mal outillées face aux enjeux environnementaux. Effectivement, l’Ordre des travailleurs sociaux et des thérapeutes conjugaux et familiaux du Québec (OTSTCFQ) propose peu de documents, formations et activités pour soutenir ses membres. En tant qu’étudiantes à la maîtrise en travail social qui souhaitons incorporer des actions concrètes pour l’environnement dans le cadre de nos pratiques, nous proposons trois pistes d’action que l’OTSTCFQ peut suivre pour actualiser la profession du travail social au Québec. En se basant sur les approches écosociales en travail social, nos recommandations soutiennent un virage davantage écocentrique pour un domaine qui, traditionnellement, s’appuie uniquement sur la justice sociale.

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)0.009
metaresearch head score (Gemma)0.013
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: Editorial · Consensus signal: none
Teacher disagreement score0.834
Threshold uncertainty score0.342

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0260.021
Scholarly communication0.0120.006
Open science0.0030.008
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0230.003

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 head0.030
GPT teacher head0.363
Teacher spread0.333 · 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
GenreEditorial

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
Published2024
Admission routes2
Has abstractyes

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