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Record W4413786308 · doi:10.1007/s10919-025-00491-2

Face Masks and Interpersonal Perceptions: Null Effects on Perceived Trust and Credibility

2025· article· en· W4413786308 on OpenAlexaff
Norah E. Dunbar, Vincent Denault, Christopher D. Otmar, Gordon Abra

Bibliographic record

VenueJournal of Nonverbal Behavior · 2025
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCredibilityWitnessPsychologyTrustworthinessInterpersonal communicationSocial psychologyDistancingPerceptionFace (sociological concept)Face masksPriming (agriculture)DeceptionPublic trustApplied psychologyInternet privacyCoronavirus disease 2019 (COVID-19)Public relationsMedicinePolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

Abstract During the COVID-19 pandemic, many courtrooms implemented physical distancing protocols, including face-mask requirements for testifying witnesses. These precautions prompted debate among academics and practitioners about whether face coverings might impair jurors’ ability to assess a witness’s credibility. To examine this question, we conducted two experimental studies using simulated video-recorded testimony. We hypothesized that face masks would reduce perceived credibility as measured by ratings of trustworthiness and overall witness credibility. Across both studies, our results provided no evidence that face masks diminished perceptions of credibility or trust. These results suggest that, in such settings, face mask use may not significantly interfere with jurors’ interpersonal assessments. We discuss implications for future courtroom procedures should witnesses be required to wear a medical mask, either for public or personal health reasons.

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)0.007
metaresearch head score (Gemma)0.071
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 score0.010
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.071
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.026
GPT teacher head0.314
Teacher spread0.288 · 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
Published2025
Admission routes1
Has abstractyes

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