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Record W4398142535 · doi:10.1080/1068316x.2024.2352700

Exploring the role of emotional demeanor in a preliminary investigation context: expectation violations & gender

2024· article· en· W4398142535 on OpenAlexaff
Alisha C. Salerno‐Ferraro, Regina A. Schuller

Bibliographic record

VenuePsychology Crime and Law · 2024
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsYork University
Fundersnot available
KeywordsPsychologyContext (archaeology)Social psychologyCognitive psychologyDevelopmental psychologyHistory

Abstract

fetched live from OpenAlex

In a criminal investigation, displaying an unexpected emotional demeanor could trigger suspicion or perceptions of involvement. Across two studies, mock investigators read a case summary of a preliminary investigation where emotional demeanor (expected/unexpected) and gender of a person of interest (POI) in the investigation (man/woman) were systematically varied. In the second study, a cognitive busyness manipulation was included. In Study 1 (n = 420), an unexpected emotional demeanor led to lower ratings of appropriateness, negative affect display, credibility, and inflated perceptions of suspicion. These results were replicated in Study 2. In Study 2 (n = 396), results showed both main and interaction effects for both emotional demeanor and gender on judgments of several relevant evaluations including suspicion, involvement, and credibility, evidencing the influence of emotional demeanor at a very early stage of investigation, particularly for females. Cognitive busyness did not affect any evaluations of the case or POI.

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)0.041
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 score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.141
GPT teacher head0.359
Teacher spread0.219 · 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
Published2024
Admission routes1
Has abstractyes

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