Developing Community Capacities through Scenario Planning for Natural Resource Management: A Case Study of Polar Bears
Bibliographic record
Abstract
Polar bears (Ursus maritimus) were listed as a threatened species in Ontario in 2009 as a precautionary measure based on the expectation that their sea ice habitat will decline. The authors studied the Swampy Cree community at Fort Severn, which traditionally harvests this species, to assess community adaptive and governance capacities and designed and discussed four future scenarios regarding potential uses and management strategies for polar bears. The goal of the scenario planning exercise was to broaden community discussions of how to interact with the government regarding polar bear management. Community actions subsequent to the exercise were more proactive, indicating that the exercise successfully encouraged new thinking. We conclude that (1) scenarios create space for the discussion of options that were previously discounted, and (2) scenario planning is a useful tool for the empowerment of communities for the development of adaptive governance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".