The Drivers of the Life Satisfaction of Pro-environment and Non-pro-environment People
Bibliographic record
Abstract
Happiness or life satisfaction tends to be the ultimate goal of human beings. It is the intention of this study to investigate the factors influencing the life satisfaction of pro-environment and non-pro-environment people. The data were collected from interviews with 320 adults, who were equally divided into pro-environment and non-pro-environment adults of at least 18 years of age and that had come to the randomly-selected shopping centers in Bangkok. The life satisfaction of the people, regardless of their environmentally-friendly attitude, was assumed to be driven by the personal characteristics of optimism and internal locus of control, and the domains of life of family, personal health, self-actualization, and material possessions, as well as the altruistic and biospheric values of being nature lovers. The results from the t-test suggested that pro-environment people are likely to be more satisfied with their lives than non-pro-environment people. Moreover, the multiple regression analysis indicated that the life satisfaction of the pro-environment people was positively influenced by the biospheric value of being a nature lover, self-actualization, and age, and was negatively influenced by education. The pro-environment people that were never married were more satisfied with their lives than those that were married. Finally, the life satisfaction of the non-pro-environment people was positively triggered by the personal characteristics of optimism and having an internal locus of control, as well as the domain of life of personal health and age.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".