Interpersonal Trust across Six Asia-Pacific Countries: Testing and Extending the ‘High Trust Society’ and ‘Low Trust Society’ Theory
Bibliographic record
Abstract
BACKGROUND: Trust is regarded as a necessary component for the smooth running of society, although societal and political modernising processes have been linked to an increase in mistrust, potentially signalling social and economic problems. Fukuyama developed the notion of 'high trust' and 'low trust' societies, as a way of understanding trust within different societies. The purpose of this paper is to empirically test and extend Fukuyama's theory utilising data on interpersonal trust in Taiwan, Hong Kong, South Korea, Japan, Australia and Thailand. This paper focuses on trust in family, neighbours, strangers, foreigners and people with a different religion. METHODS: Cross-sectional surveys were undertaken in 2009-10, with an overall sample of 6331. Analyses of differences in overall levels of trust between countries were undertaken using Chi square analyses. Multivariate binomial logistic regression analysis was undertaken to identify socio-demographic predictors of trust in each country. RESULTS: Our data indicate a tripartite trust model: 'high trust' in Australia and Hong Kong; 'medium trust' in Japan and Taiwan; and 'low trust' in South Korea and Thailand. Trust in family and neighbours were very high across all countries, although trust in people with a different religion, trust in strangers and trust in foreigners varied considerably between countries. The regression models found a consistent group of subpopulations with low trust across the countries: people on low incomes, younger people and people with poor self-rated health. The results were conflicting for gender: females had lower trust in Thailand and Hong Kong, although in Australia, males had lower trust in strangers, whereas females had lower trust in foreigners. CONCLUSION: This paper identifies high, medium and low trust societies, in addition to high and low trusting population subgroups. Our analyses extend the seminal work of Fukuyama, providing both corroboration and refutation for his theory.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".