The inner-city inhabitants and foreigners' contribution commitment to improving air pollution: Dataset from a field survey in Hanoi
Bibliographic record
Abstract
Science-based desirable solutions for mitigating and or reducing environmental pollution are of important priorities for many developed and developing countries. This article is conducted to better understand how well the inner-city citizens and foreigners perceive air pollution, how well they respond to it, and how much they willingly contribute to improving air quality in Vietnam, a lower-middle-income nation in Southeast Asia. During late 2019, a stratified random sampling technique and a contingent valuation method (CVM) were employed to survey 199 inhabitants and 75 foreigners who reside and travel within the inner-city of Hanoi, respectively. The data comprises four major groups of information, including (1) perception of air pollution and its impacts, (2) preventive measures used to mitigate polluted air, (3) commitments on willingness-to-pay (WTP) for reducing air pollution alongside reasons for the yes-or-no-WTP decision, and (4) demographic information of interviewees. The data could offer many attributes and policy implications for better economics and environmental management in the study area and or in similar places around the world.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".