Preference for modernization is universal, but expected modernization trajectories are culturally diversified: A nine-country study of folk theories of societal development
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".