Factors Influencing the Willingness to Pay for Entrance Permit: The Evidence from Taman Negara National Park
Why this work is in the frame
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Bibliographic record
Abstract
Non-market techniques such as Travel Cost Method (TCM) and Contingent Valuation Method (CVM) are commonly used to estimate the economic benefits of outdoor recreation. This study applied the CVM, with Willingness to Pay (WTP) as the elicitation method, to investigate the pattern of willingness to pay among visitors of Taman Negara National Park (TNNP). In applying CVM, the respondents were asked on the maximum amount they were willing to pay to enter this park. Data were obtained using closed-ended questionnaires through interview. About 196 visitors were involved in the study. This study used multiple regressions (MR) to investigate factors that determine WTP for entrance permit in TNNP. This study found that the WTP was positively related to several important factors; and these factors include nationality, income, education and marital status. All these factors can help to explain the WTP for entrance permit at TNNP. Approach in determining WTP for entrance permit will help park authorities to be more financially self-sufficient. In addition, it will generate more income, and thus more efficiency in operating and maintaining the national parks. Keywords: Willingness to pay, Contingent Valuation Method, multiple regressions, national park, entrance permit
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it