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Record W4383220223 · doi:10.7202/1100661ar

LA CONFIANCE DANS LA RELATION D’AIDE : UN DONNÉ OU UNE PRATIQUE D’ACCOMMODEMENT À CONSTRUIRE ?

2023· article· fr· W4383220223 on OpenAlexvenueno aff
N’Dri Paul Konan, Charlotte Jeanrenaud, Marcia Neves Pereira, Amandine Pellegrinelli, N. Mangold, Joana Da Rocha Lopes

Bibliographic record

VenueCanadian social work review · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicMulticulturalism, Politics, Migration, Gender
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

La relation de confiance est appréhendée dans la plupart des champs de pratiques du travail social comme un postulat capital de la relation d’aide. Or, aussi bien du point de vue du sens commun que de celui de la littérature sur le sujet, la confiance ne se présente pas comme un donné, mais comme une pratique d’accommodement à construire et dont il convient d’identifier les déterminants et les conditions de mise en oeuvre. Comment les professionnel(le)s s’y prennent-ils pour construire la relation de confiance avec les usagers et usagères dans leurs champs d’intervention? Cet article présente les résultats d’une recherche qui tente de répondre à cette question dans deux champs du travail social : l’aide aux femmes requérantes d’asile victimes de violence sexuelle et les curatelles d’adultes. Les résultats mettent en évidence des pratiques d’accommodement adoptées par les professionnels et professionnelles interviewés qui sont arrimées aussi bien aux contextes de pratique qu’aux réalités des usagers et usagères. Ces pratiques combinent et s’appuient sur un ensemble de savoirs, savoir-faire et savoir-être permettant l’émergence, la construction et le maintien du lien de confiance.

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)0.019
metaresearch head score (Gemma)0.044
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 score0.970
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.044
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0070.023
Scholarly communication0.0080.010
Open science0.0020.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.128
GPT teacher head0.380
Teacher spread0.251 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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