“Wilderness” revisited: Is Canadian park management moving beyond the “wilderness” ethic?
Bibliographic record
Abstract
This paper questions whether the rescaling of conservation practice in Canada to include local and Indigenous communities, NGOs, and private market‐based actors represents a move away from wilderness‐thinking in conservation, and what implications this might have for the future of conservation in Canada. We explore the links between Cronon's “wilderness” ethic and coloniality, racism/sexism/classism, and political economy, and the extent to which recent trends in conservation practice, such as co‐management arrangements, private tourism proposals, and a shift in programming to attract a diverse public to parks, help us to move beyond the limited vision for conservation and environmentalism that the wilderness ethic provides. We interrogate the ways in which the concept of wilderness is being employed, resisted, and transformed by a multitude of actors in three parks and conservation areas across Canada. We argue that although recent developments in conservation practice help to redress some of the worrisome aspects of wilderness‐thinking in parks, they also reinforce and re‐emphasize problematic lines of thinking and praxis. While the wilderness character of Canadian parks has shifted a great deal since the turn of the 20thcentury, the wilderness ethic remains deeply embedded within conservation discourse and practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.021 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".