Valuing Urban Tropical River Recreation Attributes Using Choice Experiments
Bibliographic record
Abstract
While providing public access to rivers in urban areas is a first step, maintaining a high quality recreation experience can be expensive. Knowing the economic benefits of high quality recreation may help recreation managers in justifying budget increases and define priorities during a time of scarce resources. To provide that information we have conducted urban river recreation valuation using Choice Experiments (CE). We value user defined recreation attribute improvements for the following: reducing the presence of trash, increasing water clarity, reducing crowds and increasing vegetation. We also tested whether pro-environmental attitudes and behaviors influence visitors’ Willingness to Pay (WTP) for improvements in environmental attributes. Three of the four attribute improvements were statistically significant (marginal values are provided in parenthesis): reduction of trash ($173), improving water clarity ($52), and reducing crowding ($28). The results can help managers justify improved trash removal and littering enforcement strategies, and advocate improvement of water quality by means of enacting and enforcing more strict regulations on littering, off-roading use, gravel pit discharges, and maximum visitation levels.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".