Running head: MANAGING THE ‘COMMONS’- A STATED CHOICE ANALYSIS Managing the “Commons ” on Cadillac Mountain: A Stated Choice Analysis of Acadia National Park Visitors ’ Preferences
Bibliographic record
Abstract
authors would like to thank Don Anderson of StatDesign Consulting in Evergreen, Colorado for his assistance developing the experimental design used in this study. The authors would also like to thank Aurora Moldovanyi and Brett Kiser for there assistance in administering the study questionnaire, Charlie Jacobi and other park staff at Acadia National Park for their contribution to this research, Dr. Robert Manning of the University of Vermont for his involvement in the study, and Drs. Joe Roggenbuck and Jeff Marion of Virginia Tech for their scholarly review of this manuscript. Finally, the authors would like to thank Len Hunt of the Ontario Ministry of Stated choice analysis was used to assess visitors ’ preferences for alternative combinations of public access, resource protection, visitor regulation, and site hardening to manage the Cadillac Mountain summit. Results provide insight into visitor preferences concerning the management of national park icon sites like the summit of Cadillac Mountain. These areas have received limited research attention. Results suggest that visitors consider resource protection to be a priority and are willing to accept regulation of visitors ’ behavior onsite reinforced with the use of moderately to highly intensive management structures, but generally don’t support limiting the freedom to
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".