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Record W2140797620 · doi:10.1177/0146167213508151

Physical Objects as Vehicles of Cultural Transmission

2013· article· en· W2140797620 on OpenAlexafffundabout
Keiko Ishii, Yuri Miyamoto, Nicholas O. Rule, Rie Toriyama

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2013
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Toronto
FundersJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of Canada
KeywordsHarmony (color)PsychologySocial psychologyUniquenessCultural valuesCultural transmission in animalsDevelopmental psychologySociologyGender studies

Abstract

fetched live from OpenAlex

We examined how cultural values of harmony and uniqueness are represented and maintained through physical media (i.e., colorings of geometric patterns) and how individuals play an active role in selecting and maintaining such cultural values. We found that colorings produced by European American adults and children were judged as more unique, whereas colorings produced by Japanese adults and children were judged as more harmonious, reflecting cultural differences in values. Harmony undergirded Japanese participants' preferences for colorings, whereas uniqueness undergirded European American participants' preferences for colorings. These cultural differences led participants to prefer own-culture colorings over other-culture colorings. Moreover, bicultural participants' preferences acculturated according to their identification with their host culture. Furthermore, child rearers in Japan and Canada gave feedback about the children's colorings that were consistent with their culture's values. These findings suggest that simple geometric patterns can embody cultural values that are socialized and reinforced from an early age.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.073
GPT teacher head0.391
Teacher spread0.319 · 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

Citations32
Published2013
Admission routes3
Has abstractyes

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