Bibliographic record
Abstract
I am deeply indebted to many people who gave generously of their time and shared their expertise as I sought answers to questions.Since one of the challenges that confronted me was the paucity of published material on the policy process for parks, the willingness of many people to share their knowledge was an essential foundation for this book.Numerous Parks Canada officials were enormously helpful, providing the detail, expert knowledge, and insight that allowed me to develop the arguments I offer.In the early stages of my work, two individuals were particularly useful.Harold Eidsvik had been one of two planners in the original planning section for parks in the late 1950s, and he guided me through the documents, ideas, and experiences that led to the 1964 Policy Statement.Bruce Amos, former director of parks establishment, patiently explained that I "could not get there from here" -parks were not quite the instruments I initially took them to be.These two men provided the primer that enabled me to see parks more clearly and build the arguments on which the analysis grew.Many others at Parks Canada, including several who are retired, explained their roles and experiences in the evolution of policy development.My research was also facilitated by employees in a number of federal departments: the Canadian Wildlife Service, the Department of Energy, Mines and Resources, the Treasury Board Secretariat, and the Privy Council Office.The Academic Access Program of the Privy Council Office was essential for helping me answer questions for which no published sources existed.Interest group leaders were equally helpful.In particular, the late Gavin Henderson, founder of the National and Provincial Parks Association of Canada, explained the early details and challenges of the parks protection movement.His award as a Member of the Order of Canada recognizes his work for wilderness conservation in this country.In addition, people in the Canadian Parks and Wilderness Society, the World Wildlife Fund, the Sierra
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.195 | 0.144 |
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".