Pro-Environmental Behavior and Social Capital in Indonesia 2021: A Micro Data Analysis
Bibliographic record
Abstract
Pro-environmental behavior (PEB) is one of the individual efforts to provide public goods.This study differs from previous research in three basic aspects, starting from a multidimensional approach that simultaneously considered different PEB (energy savings, vehicle use, waste reduction, and water savings), included social capital as measured by 26 indicators, and the development of the PEB index and social capital with the CATPCA.Based on the data of 2021 Happiness Level Measurement Survey by Statistics Indonesia, this study found that social capital is an important and significant driver of PEB.In particular, social participation had the greatest effect followed by trust in government, trust in neighbors and tolerance.Other factors showed varying results; PEB was displayed more by women than men, rural people than urban people and people with a partner than those without.In addition, PEB improved with older age while increase in income and education decreased PEB.Based on the findings, this study suggested the government to take part in promoting an increase in social capital through the implementation of various joint activities/events in the neighborhood.In addition, the government and environmental protection organization can begin to voice the cost savings that can be achieved with PEB.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".