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Record W4226164621 · doi:10.1016/j.ssaho.2022.100264

Do demographic predictors of personal values vary by context? A test of Schwartz's value development theory

2022· article· en· W4226164621 on OpenAlexaff
Andrew Miles, Catherine Yeh

Bibliographic record

VenueSocial Sciences & Humanities Open · 2022
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsValue (mathematics)Context (archaeology)Social value orientationsSocial environmentCultural valuesSocial psychologyTest (biology)MacroPsychologySociologyWorld Values SurveyMacro levelPositive economicsSocial scienceGeographyEconomics

Abstract

fetched live from OpenAlex

Personal values have been shown to shape consequential social beliefs and behaviors in cultures around the world, but relatively little is known about how they develop. Schwartz argues that personal values form as individuals are exposed to social institutions which are themselves informed by cultural values and other macro-level forces. An important but largely unexamined implication of this theory is that nominally similar social categories such as gender or religion will have different effects in different countries because context-sensitive institutions will imbue them with context-specific meanings. We test this claim using eight independent, nationally representative samples that collectively include 32 European countries (total N = 374,729). We find that most relationships between social categories and personal values vary across countries. Further, country-specific effects are patterned by cultural regions, supporting the idea that similarities in macro-level influences lead to similarities in how social institutions shape personal values. These results are consistent with Schwartz's theory. Practically, they suggest that efforts to understand value development should be sensitive the particulars of specific environments.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.115
GPT teacher head0.361
Teacher spread0.246 · 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 designQualitative
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

Citations6
Published2022
Admission routes1
Has abstractyes

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