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Record W1630849361 · doi:10.3917/aj.451.0074

Paris-Montréal. Des façons d'accompagner le « choc terrible » du décès d'un enfant dans les années 1960-1980

2012· article· fr· W1630849361 on OpenAlexaffabout
Martin Messika

Bibliographic record

VenueArchives Juives · 2012
Typearticle
Languagefr
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsJudaismSocial workSociologyContext (archaeology)Service (business)Order (exchange)Gender studiesPolitical scienceHistoryLawEconomyBusiness

Abstract

fetched live from OpenAlex

Jewish migration from North Africa towards France beginning in the 1950s brought along with it a major mobilization of Jewish communal institutions and in particular Jewish social service agencies. Their activities must be understood within the larger context of the evolution of social work practices. In order to bring to light these particularities, this article uses a comparative perspective. It looks as the way in which social workers from two Jewish organizations, one in Paris and one in Montreal, reacted when faced with the death of a child within a family being followed by their respective agencies. By studying the individual case files of the Comité d’Action Social Israélite de Paris and the Jewish Immigrant Aid Services in Montreal, this article explores the place of emotions in the monitoring of « clients » of these social service agencies. This case study enables us to take into account how emotions were dealt with following a period of mourning, as a dimension of social work.

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)0.000
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.061
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0560.003

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 head0.020
GPT teacher head0.206
Teacher spread0.186 · 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
Published2012
Admission routes2
Has abstractyes

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