Comparing Recreation Benefits from On-Site versus Household Surveys in Count Data Travel Cost Demand Models with Overdispersion
Why this work is in the frame
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Bibliographic record
Abstract
On-site surveys of tourists often lead to overestimates of annual tourism because tourists who are frequent repeat visitors are more likely to be sampled. This unrepresentative sample leads to statistical problems known as ‘truncation’ and ‘endogenous stratification’ in widely used travel cost demand models. Further, wide variation in the number of on-site visits among tourists can lead to overdispersion in the dependent variable of count data travel cost models. The authors present the first real-world data correction for all three problems and compare the corrected estimates with the ideal household survey. Correcting for truncation and endogenous stratification in a count data specification allowing for overdispersion (negative binomial specification) lowers the demand and benefit estimate to a mean value not significantly different from the household estimate. If tourism researchers wish to develop visitor use estimates from on-site surveys consistent with household level surveys, the authors' improved demand estimators would allow them to do so with some confidence in the results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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