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Record W4220893899 · doi:10.1111/ajsp.12533

Preference for modernization is universal, but expected modernization trajectories are culturally diversified: A <scp>nine‐country</scp> study of folk theories of societal development

2022· article· en· W4220893899 on OpenAlexaffabout
Kuba Kryś, Colin A. Capaldi, Yukiko Uchida, Katarzyna Cantarero, Cláudio Torres, İ̇dil Işık, Victoria Wai Lan Yeung, Brian W. Haas, Julien Teyssier, Laura Andrade, Patrick Denoux, David O. Igbokwe, Agata Kocimska‐Zych, Léa Villeneuve, John M. Zelenski

Bibliographic record

VenueAsian Journal Of Social Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsCarleton University
FundersJapan Society for the Promotion of ScienceNarodowe Centrum Nauki
KeywordsModernization theoryOpenness to experiencePovertyChinaPreferenceSociologyPsychologyEconomic growthPolitical scienceDevelopment economicsSocial psychologyEconomics

Abstract

fetched live from OpenAlex

Cultural sensitivity in societal development has been advocated for since at least the 1960s but has remained understudied. Our goal is to address this gap and to investigate folk theories of societal development. We aimed to identify both universal and culturally specific lay beliefs about what constitutes good societal development. We collected data from 2,684 participants from Japan, Hong Kong (China), Poland, Turkey, Brazil, France, Nigeria, the USA, and Canada. We measured preferences for 28 development aims. We used multidimensional scaling, analysis of variance, and pairwise comparisons to identify universal and country‐specific preferences. Our results demonstrate that what people understand as modernization is fairly universal across countries, but specific pathways of development and preferences towards these pathways tend to vary between countries. We distinguished three facets of modernization—foundational aims (e.g., trust, economic development), welfare aims (e.g., poverty eradication, education), and inclusive aims (e.g., openness, gender equality)—and incorporated them into a folk meta‐theory of modernization. In all nine countries, the three facets of modernization were preferred more than conventional aims (e.g., military, demographic growth). We propose a method of implementing our findings into a culturally sensitive modernization index.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.094
GPT teacher head0.352
Teacher spread0.259 · 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 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

Citations13
Published2022
Admission routes2
Has abstractyes

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