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Record W4235536089 · doi:10.32920/ryerson.14664984.v1

Cross-cultural emotion recognition in adults and children

2021· preprint· en· W4235536089 on OpenAlexaffabout
Belle Nicole Reyes

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsCross-culturalPsychologyEthnic groupCross-cultural studiesImmigrationEmotion recognitionDevelopmental psychologySocial psychologyGeographyAnthropologySociology

Abstract

fetched live from OpenAlex

The current studies investigated cross-cultural emotion recognition in South Asian and Caucasian Canadian adults and children. The two main goals of the current research were to disentangle the effects of culture and race on cross-cultural emotion recognition and to chart the development of cross-cultural differences in emotion recognition. Both adults and children completed an emotion recognition task, viewing faces of four different racial/cultural groups (South Asian Canadians and immigrants, Caucasian Canadian and immigrants). Adults completed a cultural identification task with these four racial/cultural groups and a contact questionnaire that assessed their exposure to Caucasian and South Asian individuals. Findings revealed that Caucasian and South Asian Canadian adults showed cross-cultural differences in emotion recognition; however, children did not. Furthermore, adults were able to identify the cultural background of Caucasian and South Asian faces at above-chance levels. Finally, results indicated that higher levels of cross-cultural exposure were related to improved cross-cultural emotion recognition for Caucasian adults only.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.341
Threshold uncertainty score0.678

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.055
GPT teacher head0.324
Teacher spread0.269 · 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
Published2021
Admission routes2
Has abstractyes

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