Divergent values and perspectives drive three distinct viewpoints on grizzly bear reintroduction in Washington, the <scp>United States</scp>
Bibliographic record
Abstract
Abstract The success or failure of apex carnivore reintroduction efforts can hinge on understanding and attending to diverse viewpoints of those involved in and impacted by reintroductions. Yet, viewpoints vary widely due to a suite of complex and intersecting factors, such as values, beliefs and sociocultural context. We ask, ‘what are the diverse viewpoints that exist surrounding apex carnivore recovery and what kinds of emotional, analytical and values‐based judgments might people use to construct their viewpoints?’ We used Q‐methodology to identify distinct, generalized viewpoints and areas of overlap and divergence between them, surrounding a proposal to reintroduce grizzly bears ( Ursus arctos horribilis ) to the North Cascades Ecosystem, USA. Q‐methodology combines qualitative and quantitative methods by asking purposefully sampled respondents to sort various statements on a given topic into an ordered grid. We found three distinct viewpoints among 67 respondents using factor analysis and responses to open‐ended questions about the sorting exercise. Two of these viewpoints represent essentially polarized perspectives corresponding to deeply normative notions about grizzly bear recovery, where one views reintroducing bears as a moral requisite, and the other views it as inappropriate and risky. These viewpoints primarily diverged on their perceptions of risk and perspectives about our collective responsibilities to and appropriate relationships with others (i.e. ‘relational values’). The third viewpoint was distinguished by its prioritization of practical considerations and views reintroducing bears as impractical and not sensible . Our analysis underscores the need to identify and attend to latent viewpoints that may be overlooked in the polarized public discourse as well as the multiple value systems and perceptions of risk that are integrated in perspectives on grizzly bear reintroduction. Additionally, our broadly defined identity groups were of very little utility in predicting viewpoints in this study, highlighting the importance of avoiding assumptions about people's views based on their identities and interests. We argue that forefronting conversations about responsibilities and appropriate relationships is critical for finding acceptable paths forward in such recovery efforts. We discuss the management implications of these findings for the North Cascades grizzly bear reintroduction, and for other large carnivore reintroductions. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".