Deconstructing ecological integrity policy in Canadian national parks
Bibliographic record
Abstract
Criticisms of the traditional preservationist ‘fortress park’ model for protected areas policy are numerous and well documented (Janzen, 1989; Pretty, 1997; Neumann, 1998; Adams and Hulme, 2001; Pimbert and Risby, 2002; Bengtsson et al., 2003; GEF, 2005), and ecological integrity has been promoted as a more enlightened principle for conservation policy formation (e.g. Woodley et al., 1993). Numerous definitions of ecological integrity exist (Karr, 1991; Kay, 1991; Woodley et al., 1993; Westra, 1994) and though some commentators question the adequacy of ecological integrity as a policy goal (Wicklum and Davies, 1995), relatively few authors have examined what this plurality of definitions means for practice (DeLeo and Levin, 1997; Manuel-Navarette, 2003). This chapter describes the transformation of ecological integrity theory into policy and practice in the Canadian national park system, and examines specific park management problems in Canada that illustrate difficulties arising from applying a narrow interpretation of ecological integrity to protected area management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.051 | 0.002 |
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; both teacher heads agree on what is shown here.
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".