Assessing the changing effects of forest harvesting on nature-based tourism: a case of sport-fishing tourism in Ontario, Canada
Bibliographic record
Abstract
Forest harvesting can negatively affect nature-based tourism operations. Using observable and interpretable indicators of operating tourism establishments and associated prices charged for fishing packages, we illustrate how one can assess these forest harvesting effects. From a case of floatplane-accessible tourism in Ontario, Canada, we found no evidence to implicate recent (less than 10 years) forest harvests in decisions by tourism operators to close their establishments between 2000 and 2010. Using a hedonic price analysis, we found a significantly reduced effect of forest harvests on prices charged by these tourism operators between 2000 and 2010. These conclusions were robust to different specifications of forest harvesting. On the one hand, the results suggest that changes to forest management planning, policies, and practices in Ontario appear to have mitigated the negative effects from forest harvesting on nature-based tourism. On the other hand, the results show a method that other researchers and policy analysts can adopt to monitor the changing effects of forest management on economic activities such as nature-based tourism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".