National parks best practices: Lessons from a century's worth of national parks management
Bibliographic record
Abstract
While the importance of ecological conservation and encouraging public recreation in national parks is widely recognized, challenges to achieving these goals persist. With over a century of national park management experience, the institutional knowledge of national park systems in Australia, Canada, New Zealand, and the United States can offer a valuable insight into management best practices. Twelve open-ended semistructured interviews with national park experts representing the four systems revealed valuable lessons learned in major facets of national park management. Overall, our results suggest that effective and sustainable national park management requires federally-based organizational framework with deference to local institutions at park-level, stakeholder inclusion in park management decision-making, public engagement encouraged by information-sharing and education, clarity on boundaries to improve relations with adjacent land owners, and prioritizing improved indigenous relations. Interviews highlighted that better park governance is rooted in education to raise awareness of the importance of national parks and park systems to the public. Tourism and climate change were widely anticipated to increasingly pose challenges to park management, underscoring a shared urgency to address these issues.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".