The social benefits of increasing protected natural areas: an Eastern Canadian case study using the contingent valuation method
Bibliographic record
Abstract
We examined the sensitivity of social benefits to the amount (scope) of protected natural areas (PNAs) in the Eastern Canadian province of New Brunswick using the contingent valuation method. Household willingness-to-pay responses were elicited under three valuation scenarios: (1) maintaining the existing amount of PNAs at 2 per cent of the provincial land base; (2) increasing the PNAs to 8 per cent of the provincial land base; (3) increasing the PNAs to 14 per cent of the provincial land base. Under these scenarios, mean willingness-to-pay values were estimated at $58.63, $66.57 and $71.29 (CDN) per household per year, respectively. While these mean values initially indicated that social benefits were sensitive to scope, analysis of values within the 95% confidence interval revealed scope insensitivity. The possible sources and implications of these findings are discussed in the context of future benefit–cost analyses associated with PNA policies in the province.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".