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Record W4386899787 · doi:10.3917/tt.042.0123

Monétariser le travail de care des proches

2023· article· fr· W4386899787 on OpenAlexaff
Olivier Giraud

Bibliographic record

VenueTerrains & travaux · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Policies and Family
Canadian institutionsMinistère de l’Emploi et de la Solidarité Sociale (Québec)
Fundersnot available
KeywordsHumanitiesPolitical scienceSociologyArt

Abstract

fetched live from OpenAlex

Cet article analyse différentes logiques de réception des catégories d’action publique qui organisent, et surtout monétarisent, le travail de care réalisé par les aidant·e·s familiaux·ales. Sur la base de la grille d’analyse de la sociologie de l’expérience de François Dubet, trois logiques d’action saillantes correspondant aux modes d’investissement du travail de care familial par les proches aidant·e·s en lien avec les catégories d’action publique qui organisent ce travail sont distinguées. Ces catégories d’action publique comportent des aspects identitaires et de régulation. Avant de procéder à cette analyse, l’article rappelle l’inscription sociale tendue du travail de care entre normes familiales et normes instituées par l’action publique, présente une estimation du nombre des aidant·e·s familiaux·ales monétarisé·e·s en France, rappelle le contexte méthodologique d’acquisition du matériau empirique qui sert de base à l’analyse puis procède à une analyse des catégories d’action publique qui soutiennent les aidant·e·s familiaux·ales et organisent leur monétarisation. La dernière partie de l’article présente les résultats de l’analyse en distinguant les logiques d’action saillantes de l’intégration à des normes familiales, d’une rationalité stratégique et d’une subjectivation singulière.

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)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.095
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.008
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0010.002
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 head0.047
GPT teacher head0.316
Teacher spread0.269 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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