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Record W4376134308 · doi:10.7202/1099331ar

Représentations sociales de l’assurance : étude comparative chez les étudiants français et sénégalais

2023· article· fr· W4376134308 on OpenAlexvenueno aff
Boubacar Coulibaly, Jean-Claude Étoundi, Pahlavan Farzaneh

Bibliographic record

VenueAssurances et gestion des risques · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesSociologyArt

Abstract

fetched live from OpenAlex

L’objectif de ce travail est d’étudier comparativement la représentation sociale de l’assurance chez les étudiants français et sénégalais. Partant de l’hypothèse d’une représentation sociale différente induite par le contexte social, l’étude a été réalisée en France (N = 186) et au Sénégal (N = 136). Les données collectées par le biais de questionnaires d’associations libres ont été soumises à une analyse hiérarchique. Les résultats présentés dans cette étude, par les termes qui constituent le noyau central, montrent que la représentation sociale de l’assurance est considérée, aussi bien chez les étudiants français que sénégalais, comme un moyen de protection car source de sécurité et de confiance. Cependant, les participants des deux groupes se distinguent sur la primauté accordée à certaines dimensions spécifiques de la représentation. Si les participants français axent leur discours sur les différents types de contrat ainsi que les aspects financiers des assurances, leurs homologues sénégalais s’attardent davantage sur la mission de l’assurance dans la société.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.287
Threshold uncertainty score0.570

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0030.003
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.150
GPT teacher head0.418
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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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