Interpersonal Trust for Different Domains and Risk Levels:Comparing the Trust Circle of Chinese and Canadians
Bibliographic record
Abstract
In everyday life,varied economic and information exchanges frequently occur between one and another,a primary condition of which is both parities trusting each other.In the study,a new construct named as was developed and the size of the circle was used as an index of one's interpersonal trust level.The purpose of the study was to explore how the size of one's trust circle is affected by the trust domain,risk level and culture in China and Canada.An questionnaire was administrated to 100 Chinese residents in China(mean age 32.92) and 102 Canadians(mean age 31.94) who participated in the study voluntarily.The results showed that:(1) the average trust circle size in different domains differs,specifically,the size of the trust circle in the economic situation is the largest,the size is the smallest in the negative information sharing context and it is in the middle in the positive personal/social information sharing context;(2) the trust circle size varies with risk level;as the economic risk increases the trust circle becomes smaller,as the degree of the shared positive information increases the trust circle becomes larger,and as the degree of the shared negative information increases the trust circle becomes smaller;(3) the trust circle sizes in different cultures are not the same,for example,trust circle size of Chinese participants is larger than Canadians' in the economic risk situation,and Canadians' trust circle sizes are larger than Chinese counterparts' in both positive and negative personal/social information sharing domains.Thus,it can be concluded that whether people trust others is affected by the related domain,the risk level,the culture and interactions of two or three factors.Aforementioned results are heuristic for clarifying culture differences of interpersonal trust and for promoting cross-culture communication of people from China and Canada.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".