COOPERATIVE MANAGEMENT IN NATIONAL PARKS
Bibliographic record
Abstract
The last decade has brought many innovations to cooperative park management approaches used by park agencies and aboriginal peoples. This paper briefly reports on cooperative management efforts in six northern Canadian national parks, Wapusk, Nahanni, Tuktuk Nogait, Kluane, Vuntut and Auyuittuq, and two US national parks, Grand Canyon and Badlands. Interviews with park staff, aboriginal representatives and consultants involved with cooperative management processes serve as the source for this information. The paper is based on a series of factual and experiential questions which address: 1) park establishment; 2) history, identity and aboriginal influences on park culture and management; 3) cooperative management; 4) park and aboriginal relations, past and present; and 5) the role of consultants and other “outside ” facilitators of cooperative management. The types of arrangements for cooperative management that exist in each park are examined through the prism of the participants ’ actual experiences in the field. The paper briefly highlights different aspects of cooperative management and critical elements for its success. The subject areas include: 1) defining roles and responsibilities in cooperative management; 2) fostering communication between park agencies and aboriginal communities; 3) mechanisms for collaborative decision making; 4) establishment and maintenance of effective
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| 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".