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Record W2061055063 · doi:10.1037/a0014498

Looking into the past: Cultural differences in perception and representation of past information.

2009· article· en· W2061055063 on OpenAlexafffundabout
Li‐Jun Ji, Tieyuan Guo, Zhiyong Zhang, Deanna Messervey

Bibliographic record

VenueJournal of Personality and Social Psychology · 2009
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologySocial psychologyPerceptionChinese peopleCultural diversityChinaHistorySociology

Abstract

fetched live from OpenAlex

The authors investigated cultural differences in the way people perceive and represent temporal information. It was hypothesized that Chinese would attend to the past information more than would Canadians. In Studies 1 and 2, Canadian and Chinese participants read a description of a theft along with a list of behaviors that occurred in the past or present. Chinese participants rated behaviors that had taken place in the remote and recent past as more relevant to solving the case than did Canadians. Study 3 showed that Chinese participants recalled greater detail about past events than did Canadians. Studies 4A and 4B showed that Chinese perceived past events as being closer to the present than did Canadians, suggesting that Chinese had a greater awareness of the past. Overall, Chinese attended to a greater range of past information than did Canadians, which has significant theoretical and practical implications.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.106
GPT teacher head0.425
Teacher spread0.318 · 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

Citations146
Published2009
Admission routes3
Has abstractyes

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