Stakeholder views on grizzly bear management in the Banff-Bow Valley: a before–after Q methodology study
Bibliographic record
Abstract
Understanding stakeholder views is essential for successful wildlife management. This study used Q methodology with a before–after approach to explore stakeholder views about the problems and solutions related to grizzly bear management in the Banff-Bow Valley of Alberta, Canada. This research, conducted in 2008, followed up on a previous Q study conducted in 2004. A meta-analysis of the before and after factors revealed that some changes in views had occurred between the summers of 2004 and 2008. Interviews also supported the finding that the views of the participants had changed and revealed that the factors most frequently identified by participants as having influenced their views between the before and after Q studies were: research about grizzly bears; the occurrence of grizzly bear mortalities; and a series of “interdisciplinary problem solving” stakeholder workshops and meetings about grizzly bear management.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".