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Record W3083190228 · doi:10.46278/j.ncacn.201708132

Le paradigme de clignement attentionnel émotionnel : étude des processus attentionnels et applications dans l’étude de la parentalité

2017· article· fr· W3083190228 on OpenAlexaffvenue
Sophie-Andrée Hébert-Soucy, Karine Poitras, Benoît Brisson

Bibliographic record

VenueNeuropsychologie clinique et appliquée · 2017
Typearticle
Languagefr
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsHumanitiesPhilosophyPolitical science

Abstract

fetched live from OpenAlex

Les comportements parentaux jouent un rôle critique dans le développement des enfants. Or, peu d’études se sont intéressées aux processus attentionnels des parents, et ce, malgré des données pointant vers leur rôle dans la modulation des comportements parentaux. Le paradigme de clignement attentionnel est régulièrement utilisé afin d’examiner les mécanismes et limites de l’attention temporelle. Dans le cadre de cet article théorique, nous décrirons ce paradigme et les modèles théoriques qui le sous-tendent, puis nous présenterons diverses études afin de mettre en évidence les connaissances actuelles en lien avec le phénomène de clignement attentionnel. Un intérêt particulier sera porté au paradigme de clignement attentionnel induit par un stimulus émotionnel, afin d’examiner sa pertinence dans l’étude des processus attentionnels inhérents à la parentalité. En conclusion, la recension des écrits réalisée indique que l’utilisation de ce paradigme est prometteuse dans l’étude des processus attentionnels impliqués dans la parentalité.

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)0.006
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.053
GPT teacher head0.392
Teacher spread0.339 · 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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

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