Visitor management policy of national parks, national wildlife areas and refuges in Canada and the united states: A policy analysis of public documents
Bibliographic record
Abstract
The published visitor management policies of national parks, national wildlife areas and refuges in Canada and the United States are important components of the overall management system. This paper analyses how the visitor management policies that apply to all units operated by each agency compare to each other and compare to an ideal framework, using data from publicly‐available sources. Analysis was undertaken by policy comparison of all publicly‐available documents available in the Canadian inter‐university library system and the internet. The quantity and quality of visitor management policy is higher with higher funding levels, as demonstrated by the US National Park Service at the high end of the spectrum, and the Canadian Wildlife Service at the low end. The US National Park Service has the most comprehensive visitor management policy, and this policy is well coordinated in one overall document. The Canadian Wildlife Service has a very weak visitor management policy structure that lacks even basic goals for visitor management. Some visitor policy gaps exist for each agency. All agencies lack explicit policies governing visitor length of stay, human resources required for visitor management and economic impact measurement. This is the first policy analysis of this type undertaken. It provides a basis for the revision and improvement of these policies in the future.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".