Problem Perspectives and Grizzly Bears: A Case Study of Alberta’s Grizzly Bear Recovery Policy
Bibliographic record
Abstract
Since their threatened species listing in 2010, grizzly bear recovery has been a controversial policy issue in Alberta, Canada particularly because this charismatic carnivore represents a diverse set of values, both positive (e.g., an icon of beauty and the wilderness) and negative (e.g., a safety threat and economic risk to peoples’ livelihoods). Previous human dimensions research on grizzly bear conservation has accounted for the values and attitudes different groups of people hold for these bears, as well as their views on conflict mitigation strategies. However, the conservation literature is more limited in assessing the perspectives different people hold for grizzly bear conservation in a policy context. Arguably, understanding the policy landscape in which carnivore conservation occurs is important to achieve desired goals and objectives for species and the people expected to live with them and implement policy action. Using a case study approach between 2012-2014 and borrowing from the policy sciences problem-oriented framework, we identify the dominant problem perspectives in Alberta’s grizzly bear recovery policy using document review and interviews with participants from government, the natural resource sector, and environmental non-governmental organizations. We identify that ordinary and constitutive problem perspectives share common features across participants in this study, including frustrations with lack of policy clarity, implementation inefficiencies and committed political and financial action, and perhaps even more important, the challenges in policy decision-making and governance. We discuss the importance of meaningful engagement of people who live with large carnivores and the impacts of conservation policy, which is applicable to both a local and global scale, as success in large carnivore conservation must include the people who will ultimately implement conservation action.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.043 | 0.018 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".