Grizzly Bear Management in the Kananaskis Valley: Forty Years of Figuring It Out
Bibliographic record
Abstract
Case studies offer rich insight into the way knowledge is gathered, understood, and applied (or not) in parks and conservation contexts. This study aims to understand how knowledge and information have been used to inform decision-making about human-wildlife co-existence—specifically what knowledge has informed decisions related to grizzly bear management in the Kananaskis Valley. Focus groups of decision-makers involved in the valley’s bear program painted a rich account of decision-making since the late 1970s that was coded thematically. Our findings suggest there are typical impacts on knowledge mobilization, such as management support (or lack thereof), other agencies, capacity, and social and political pressures. In addition, the special context of the Kananaskis Valley and the forty-year timespan explored in focus group conversations provide unique lenses through which to understand knowledge mobilization. This case study reflects the barriers identified in the literature. However, the findings also include unique aspects of decision-making, such as the evolution of decision-making over a period of time in a multi-use landscape, the successful creation of networks to mediate knowledge and practice, and the creation of knowledge by practitioners.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".