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Record W3211392203 · doi:10.1111/ajsp.12513

Cultural differences in self and affect through drawings of personal experiences

2021· article· en· W3211392203 on OpenAlexaffabout
Suhui Yap, Li‐Jun Ji, Yuen Piu Chan, Zhiyong Zhang

Bibliographic record

VenueAsian Journal Of Social Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychologyFeelingAffect (linguistics)Social psychologyFace (sociological concept)Personal developmentPsychotherapistCommunicationSociology

Abstract

fetched live from OpenAlex

The present studies examined whether and how individuals from the East and West would express themselves differently in their drawings of personal success and failure. Across two studies, Euro‐Canadian and Chinese participants drew a picture depicting their personal success versus failure in the past (Study 1) or future (Study 2). Compared to the Chinese, Euro‐Canadians were more likely to express high‐arousal positive and negative affect in their drawings of personal success and failure, respectively. Replicating previous research, Euro‐Canadians also depicted a bigger “self” and were more likely to draw a face on their “self” in their drawings. Together, these findings not only demonstrated cultural differences in Euro‐Canadian and Chinese representations of their self and affective responses to personal success and failure but also suggested that pictorial representations of emotionally rich personal events can be an indirect, informative, and engaging tool for assessing cultural influences on one’s self and feelings.

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)0.003
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.094
GPT teacher head0.423
Teacher spread0.329 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

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