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Record W2535575242 · doi:10.7202/1036888ar

Les préposés aux bénéficiaires au Québec : entre amour du métier et dégoût de la tâche

2016· article· fr· W2535575242 on OpenAlexaffvenueabout
François Aubry

Bibliographic record

VenueSociologie et sociétés · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsInstitut Universitaire de Gériatrie de MontréalUniversité du Québec en Outaouais
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

L’objectif de notre article est de montrer comment l’analyse de l’activité des préposés aux bénéficiaires travaillant en centre d’hébergement au Québec permet de comprendre le processus produisant le décalage entre leur amour du métier et leur dégoût de la tâche. L’apport théorique de la psychologie de la sociologie du travail est fécond pour comprendre l’écart entre leur idéal professionnel (le prendre soin) et le travail prescrit conditionné largement par la lourde charge de travail. Nous démontrons que les préposés tentent de développer et appliquer collectivement, en situation de travail, des stratégies pour réduire cet écart, signe de leur capacité d’autonomie et d’inventivité collective. La reconnaissance de l’existence de ces stratégies et autonomie par les organisations de santé permettrait de faire éclore un débat politique sur les compétences réelles développées par ces préposés, en évitant le seul discours naturalisant ou individualisant largement répandu.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.319

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.257
GPT teacher head0.514
Teacher spread0.258 · 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

Citations5
Published2016
Admission routes3
Has abstractyes

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