'Can most people be trusted?' Understanding the cultural profile of Generalized Social Trust
Bibliographic record
Abstract
The studies presented in this thesis have been driven by a consistent finding that individuals' in different nations vary in how much they trust most others. Their central aim is to identify some of the individual and national characteristics which may account for this variation, and to assess the process of propagation of trust from a generalized to a specific other. Seven studies are reported in this thesis. Studies 1 and 2 are concerned with the development of the Generalized Social Trust (GST) scale, refining its factorial structure and cross-cultural equivalence across Romanian, British and Canadian_s~mples. A 16-item scale with a two-factorial structure (generalized social trust and distrust) reaches measurement invariance. Studies 3, 4 and 5 focus on three models of GST: a cultural model (idiocentric-allocentric values, independent and interdependent self-construals), an economic model (wealth) and a social capital model (political attitudes, participation in civic activities and voluntary organizations). Studies 3 and 5 use secondary data sources from Rounds 2 (25 nations) and 1 (21 nations) of the European Social Survey, while Study 4 includes Romanian and British samples recruited by the author. Multilevel regression analyses are used to test these models at the individual and national levels of analysis. Multiple regressions are also used in Study 4. At the individual level, the two types of self-construal (Study 4) and political attitudes (Study 5) are the best predictors of GST. At the nation level, Gross National Product (GNP) significanfly predicts trust ratings. In Studies 6 and 7, the process of propagation of GST to a specific target (ingroup member, friend of a friend and stranger) is considered in Romanian and Canadian samples. Using structural equation modelling, the main results show GST to have an indirect effect on the decision to trust via expectations of honesty, promise keeping and reciprocity. Culture moderates the effect of ingroup and relational targets on the decision to trust. While selfconstruals effects on GST are replicated, no such effects are observed in relation to the decision to trust. In conclusion, this thesis shows that some of the individual variation in GST can be best explained by one's self-construal, political attitudes and the wealth of their nation. Also, it shows that the propagation of trust from an abstract target to a specific one is fully mediated by target-specific expectations.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| 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".