Decolonial Model of Environmental Management and Conservation: Insights from Indigenous-led Grizzly Bear Stewardship in the Great Bear Rainforest
Bibliographic record
Abstract
Global biodiversity declines are increasingly recognized as profound ecological and social crises. In areas subject to colonialization, these declines have advanced in lockstep with settler colonialism and imposition of centralized resource management by settler states. Many have suggested that resurgent Indigenous-led governance systems could help arrest these trends while advancing effective and socially just approaches to environmental interactions that benefit people and places alike. However, how dominant management and conservation approaches might be decolonized (i.e., how their underlying colonial structure might be addressed, transformed, and replaced) is not always clear. Here, we describe a ‘Decolonial Model of Environmental Management and Conservation’ as an alternative paradigm to dominant approaches of conservation and management. The tenets of the model describe characteristics that might be expected of decolonized management, contrasted with those of dominant state-led approaches such as those embedded in the North American Model of Wildlife Conservation. The model does not prescribe how Indigenous governments or communities ought to govern their own territories, but instead offers insights into how external management and conservation agencies and practitioners might support (or stop impeding) Indigenous-led governance. We illustrate the model with a conservation ‘bright spot’: grizzly bear stewardship in the area now referred to as the Great Bear Rainforest in British Columbia, Canada, with a focus on work led by or in collaboration with, and within the territories of, the Haíɫzaqv, Kitasoo/Xai’xais, Nuxalk, and Wuikinuxv First Nations. While acknowledging the important context-specific variability among place-based management and conservation applications, we also discuss the model’s broader applicability.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".