Investigating urban residents’ utilization patterns and financial contribution to parks and protected areas in Vietnam: Evidence from Bayesian mindsponge mindspongeconomics
Bibliographic record
Abstract
Climate change has increasingly exerted an adverse effect on natural ecosystems and biodiversity. To be specific, one million of the world’s estimated eight million species of flora and fauna are in danger of extinction. In order to resolve the problem, a novel approach is to build an eco-surplus culture in which a group of people share a set of pro-environmental attitudes, values, beliefs, and behaviors to mitigate anthropogenic environmental impacts. In this sense, this study employs a culture-based approach to examines the antecedents of the utilization and financial contribution of urban dwellers to public and national parks where a variety of plants and animals are conserved. Using the Bayesian Mindsponge Mindspongeconomics to conduct analysis on 535 Vietnamese urban residents, we found that the frequency of visiting national park and of consuming bushmeat, the intention to visit a national park and age positively affect the willingness to contribute financially to transplantation projects in the public park. Meanwhile, having trees in the house and frequently visiting national park leads to a higher probability of people donating to conservation projects in the national park. These findings reinforce the potential of an eco-surplus culture in generating finance for conservation places that are important to curb biodiversity loss, and thus helps policy-makers to devise more suitable policies regarding urban planning and resources allocation nationwide and beyond.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".