Bibliographic record
Abstract
This paper presents new evidence linking trust and subjective wellbeing, based primarily on data from the Gallup World Poll and cycle 17 of the Canadian General Social Survey (GSS17). Because several of the general explanations for subjective wellbeing examined here show large and significant linkages to both household income and various measures of trust, it is possible to estimate income-equivalent compensating differentials for different types of trust. Measures of trust studied include general social trust, trust in management, trust in co-workers, trust in neighbours, and trust in police. In addition, some Canadian surveys and the Gallup World Poll ask respondents to estimate the chances that a lost wallet would be returned to them if found by different individuals, including neighbours, police and strangers. Our results reveal strong linkages between several trust measures and subjective well-being, as well as strong linkages between social trust and two major global causes of death—suicides and traffic fatalities. This suggests the value of learning more about how trust can be built and maintained, or repaired where it has been damaged. We therefore use data from the Canadian GSS17 to analyze personal and neighbourhood characteristics, including education, migration history, and mobility, that help explain differences in trust levels among individuals. Finally, by combining data from new dropped-wallet field experiments with survey answers about the expected return of a dropped wallet, we show that wallets are far more likely to be returned, even by strangers in large cities, than people expect.
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 | 0.002 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".