MétaCan
Menu
Back to cohort
Record W4395467022 · doi:10.1002/ejsp.3069

Changing perceptions of people wearing masks: Two years of living in a pandemic

2024· article· en· W4395467022 on OpenAlexaff
Xia Fang, Kerry Kawakami

Bibliographic record

VenueEuropean Journal of Social Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsYork University
Fundersnot available
KeywordsPsychologyAttractivenessTrustworthinessAttributionPerceptionSocial psychologyCoronavirus disease 2019 (COVID-19)Social distanceSocial perceptionCompetence (human resources)PandemicAnxietyFace masksMedicine

Abstract

fetched live from OpenAlex

Abstract Despite the widespread use of face masks to combat COVID‐19, little is known about their immediate and delayed social consequences. To understand short‐ and long‐term effects of face masks on interpersonal perception, we measured the evaluation of faces with and without masks at four time points—June 2020, January 2021, September 2021 and June 2022—from the early months of the pandemic in North America to the more recent, and from the implementation of mask mandates to the end of these requirements. Surprisingly, we found that, in general, faces with masks were perceived as more competent, warm, trustworthy, considerate and attractive, but less dominant and anxious than faces without masks. Moreover, differences in attributions of dominance, trustworthiness and warmth between faces with and without masks increased in a linear trend from June 2020 to June 2022. Notably, the impact of masks on perceptions of competence, considerateness, attractiveness and anxiousness did not change over time. We discuss how mask mandates can alter people's social perceptions of others who wear masks compared to those who do not wear masks and how these mandates may influence attributions of some traits more than others through mere exposure and/or social norms.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.384
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 teacher head, not a consensus.

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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of Social PsychologySame topicEvolutionary Psychology and Human BehaviorFrench-language works237,207