The ecology of subjective wellbeing: a global analysis of environmental factors associated with life evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it