Trust, Reciprocity, and<i>Guanxi</i>in China: An Experimental Investigation
Bibliographic record
Abstract
We examine the influence of social distance on levels of trust and reciprocity in China. Social distance, reflected in the indigenous concept of guanxi, is of central importance to Chinese culture. In Study 1, some participants participated in two financially salient trust games to measure behaviour, one with an anonymous classmate and the other with an anonymous, demographically identical non-classmate. Other participants, drawn from the same population, completed hypothetical surveys to gauge both hypothetical behaviour and expectations of others. Social distance effects on actual and hypothetical behaviour were statistically consistent. The results together corroborated the hypothesized negative relationship between trust and social distance. However, reciprocity was not responsive to social distance. Study 2 found that affect-based trust, but not cognition-based trust, played a mediating role in the relationship between social distance and interpersonal trust in a hypothetical scenario. We conclude that closeguanxities in China engender affect-based trust, which is extended toshourenclassmates. This is true despite the fact that no more cognition-based trust is placed nor reciprocity received or expected from classmates compared to demographically identicalshengrennon-classmates.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".