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Record W2171757047 · doi:10.1177/0022022103260459

The Ecocultural Framework, Ecosocial Indices, and Psychological Variables in Cross-Cultural Research

2004· article· en· W2171757047 on OpenAlexaff
James Georgas, Fons J. R. van de Vijver, John W. Berry

Bibliographic record

VenueJournal of Cross-Cultural Psychology · 2004
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsQueen's University
Fundersnot available
KeywordsIntrapersonal communicationPsychologySocial psychologyHofstede's cultural dimensions theoryContext (archaeology)IndividualismInterpersonal communicationSociologyGeography

Abstract

fetched live from OpenAlex

Relationships between context variables (ecosocial indices) and psychological variables across different nations were investigated, guided by Berry’s Ecocultural Framework. The psychological variables were values (Hofstede; Inglehart; Schwartz; Smith, Dugan, and Trompenaars) and subjective well-being (Diener). The ecosocial indices of religion and affluence had separate and in some ways contrasting relationships with psychological variables. Some religions were related to higher interpersonal power, loyalty, and hierarchy, but lower affluence. Other religions, (particularly Protestantism) and higher affluence were related to intrapersonal aspects, such as individualism, utilitarian commitment, and well-being. The most important result was the finding that scores of psychological variables showed systematic relationships with cluster membership of countries on ecosocial indices. The study proposes a solution to a theoretical and methodological problem of current cross-cultural psychology: the search for cultural (context) variables that would explain similarities and differences in psychological variables in different clusters of countries.

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)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.006
Science and technology studies0.0020.007
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0010.001
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 head0.256
GPT teacher head0.571
Teacher spread0.315 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations238
Published2004
Admission routes1
Has abstractyes

Explore more

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