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Record W3081584706 · doi:10.7202/1070662ar

Validité discriminante de l’échelle de cognition sociale et de relation d’objet (SCORS, version française) pour coter les récits TAT. Comparaison entre groupes clinique et non clinique

2020· article· fr· W3081584706 on OpenAlexvenueno aff
Cyrille Bouvet, Céline Prime, Nathalie Camart, Damien Fouques, Rafika Zebdi

Bibliographic record

VenueRevue québécoise de psychologie · 2020
Typearticle
Languagefr
FieldPsychology
TopicPsychological Testing and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyHumanitiesValidation testPhilosophyPsychometricsTest validityClinical psychology

Abstract

fetched live from OpenAlex

Cette étude a pour but d’évaluer la validité discriminante de la version française de la méthode SCORS (Social Cognition and Object Relation Scale). Procédure : les cotations SCORS de récits TAT ont été comparées entre deux groupes (non clinique et clinique). Méthode : les récits TAT de 114 participants (47 non cliniques et 67 cliniques) ont été recueillis et les planches 1, 2, 3BM et 13MF ont été cotées par deux juges indépendants. Puis nous avons comparé les résultats des deux groupes. Résultats : la fidélité interjuge entre coteurs est bonne, voire excellente suivant les échelles; il y a des différences entre les moyennes des deux groupes aux échelles de SCORS dans le sens attendu. Cela montre la validité discriminante de la version française de SCORS. Les implications cliniques et scientifiques de ces résultats sont discutées.

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)0.023
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.040
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.125
GPT teacher head0.404
Teacher spread0.279 · 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 designObservational
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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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