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Record W2121207441 · doi:10.1177/0022022109359692

Indecisiveness and Culture: Incidence, Values, and Thoroughness

2010· article· en· W2121207441 on OpenAlexaff
J. Frank Yates, Li‐Jun Ji, Takashi Oka, Ju-Whei Lee, Hiromi Shinotsuka, Winston R. Sieck

Bibliographic record

VenueJournal of Cross-Cultural Psychology · 2010
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsQueen's University
FundersHokkaido UniversityNational Science CouncilNational Science Foundation
KeywordsSocial psychologyPsychologyCognitionPoliticsMechanism (biology)Cognitive psychologyEpistemologyPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Three studies examined cultural variations in indecisiveness among Chinese, Japanese, and Americans. In Study 1, validated self-report, comprehensive measures of indecisiveness indicated large cultural differences, with Japanese participants exhibiting substantially more indecisiveness than Chinese or Americans. Study 2 provided evidence that such cultural variations correspond to variations in people’s positive versus negative values for decisive behaviors, suggesting that such values are plausibly an important means for motivating and sustaining cultural differences in indecisiveness. Study 3 provided direct behavioral instances of the differences in indecisiveness implicated in Studies 1 and 2. It also suggested that thoroughness might be an important cognitive mechanism whereby cultural differences in indecision actually occur, with thoroughness being especially prominent among Japanese decision makers. Suggestions for theory concerning the nature and foundations of indecisiveness and its cultural variations are developed and discussed, along with plausible implications for real-life practical issues, for example, in politics and management.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.291
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.482
Teacher spread0.410 · 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.

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

Citations52
Published2010
Admission routes1
Has abstractyes

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