Variations in character involving an orientation to promote good across sociodemographic groups in 22 countries
Bibliographic record
Abstract
An orientation to promote good (i.e., a disposition to take actions that contribute to the good of oneself and others) has been associated with better health and well-being outcomes. However, less is known about how orientation to promote good differs across countries and across sociodemographic groups within different countries. Using a sample of 202,898 adults from 22 diverse countries, this study examined the distribution of orientation to promote good across key sociodemographic groups within each country separately, and cross-nationally by pooling estimates across countries. Our results suggest that population mean levels of promoting good vary substantially across countries. In the pooled results, the means of promoting good also vary across most of the sociodemographic factors that we examined. Specifically, individuals who are older, female, married, employed or retired, highly educated, attending religious services frequently, and native-born reported higher means of promoting good than those in other demographic groups. In country-specific analyses, the sociodemographic variation in promoting good also differs across countries, indicating diverse societal influences. This study provides novel insights into the social distribution of orientation to promote good, which paves the way for future investigations into sociocultural influences that may shape individuals' expression of character across different national contexts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".