A study of the integration of science and park management in Victoria with reference to scientific mandates for national parks agencies in the United States and Canada
Bibliographic record
Abstract
Managing for competing uses within national parks has become increasingly difficult. Recreation and preservation are two uses for which national park managers must provide, however, to provide for both presents a dilemma. Recreational uses often lead to degradation of a park's natural resources, and therefore, compromises the preservation of the park. How should managers make their decisions? This thesis proposes that managers should formulate their management strategies using a scientific framework of data gathering and monitoring in the decision-making process. Management decisions should be based upon what provides the least amount of degradation to the park's natural resources. True knowledge upon which managers can make their decisions comes from a (1) scientific understanding of the park's ecosystems and (2) the impacts upon those ecosystems. Science provides the necessary information that leads to better knowledge of the parks resources. However, science has not always been incorporated in the management process. This thesis details why science is important and the reasons it has not been thoroughly integrated into the park's management process. It critiques the present-day integration of science in Victorian national park management, as well as Parks Victoria's management strategies. The thesis also examines the history of science and its integration into national park management by Victorian, the United States and Canadian agencies and the current attitude toward the integration of science and national park management within the three agencies. Several key figures in national park management were interviewed, and from these interviews, a story detailing the state of science in national parks developed.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.044 | 0.014 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".