Harnessing visitors' enthusiasm for national parks to fund cooperative large‐landscape conservation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Spillover impacts pose challenges for the management of protected areas (PAs). The issue of external threats encroaching on PAs has long been recognized, but a corollary—that PA conservation can increase costs borne by neighboring governments or landowners—is less well appreciated. In some contexts, basic principles of fairness and cooperation suggest that PA users should help pay these costs. Several countries have developed mechanisms for distributing the costs of spillover impacts to PA users, but not the United States. Here, we investigate whether and how US park visitors could help address one type of spillover, the need for wildlife conservation efforts beyond park boundaries, using a case study of the Greater Yellowstone Ecosystem (GYE). We examine a “conservation fee” recently proposed in the Wyoming legislature, along with tax‐based alternatives. After exploring some costs of wildlife conservation in GYE, we estimate that a fee of up to $10 per vehicle could generate up to $13 million annually, and tax‐based approaches considerably more. We consider legal, political, and governance challenges, and ways to mitigate them. The GYE could serve as a demonstration site for visitor funding of cooperative, large‐landscape conservation, for potential future expansion in the US and beyond.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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