The ecology of subjective wellbeing: a global analysis of environmental factors associated with life evaluation
Bibliographic record
Abstract
Despite a burgeoning literature on the impact of ecological indicators on wellbeing, there is a lack of research (a) into the impact of a comprehensive array of indicators, and (b) from a global perspective. This cross-sectional study redresses these issues by combining 17 indicators (which collectively offer good coverage across different aspects of ecology) with life evaluation data from three years (2020–2022) of the Gallup World Poll (n = 386,654). Through factor analysis, these indicators are clustered into three main factors: ecological needs (i.e., the quality of the environment with respect to human needs); ecological efforts (i.e., efforts to preserve or protect the environment); and ecological status (i.e. the state of the environment per se). A multilevel regression model in which individuals were nested within countries indicated an association between these factors and life evaluation, although the third (ecological status) was surprisingly in a ‘negative’ direction. We explore the significance of these findings and offer recommendations for future research.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".