Bibliographic record
Abstract
This paper has a double purpose: to see how well Durkheim's (1897) findings apply a century later, and to see if the beneficial effects of social capital on suicide prevention are parallel to those already found for subjective well-being The results show that more social capital and higher levels of trust are associated with lower national suicide rates, just as they are associated with higher levels of subjective well-being. Furthermore, there is a strong negative correlation between national average suicide rates and measures of life satisfaction. Thus social capital does appear to improve well-being, whether measured by higher average values of life satisfaction or by lower average suicide rates. There is a slight asymmetry, since the very high Scandinavian measures of subjective wellbeing are not matched by equally low suicide rates. To take the Swedish case as an example, this asymmetry is explained by Sweden having particularly high values of variables that have more weight in explaining life satisfaction than suicide (trust and quality of government), and less beneficial values of variables that have more influence in explaining suicide rates (Swedes have low belief in God and high divorce rates), because with the latest data and models the Swedish data fit the wellbeing and suicide equations with only tiny errors. If the international suicide data pose a puzzle, it is more because suicide rates, and their estimated equations, differ greatly by gender, while life satisfaction and its explanations are similar for men and women.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".