Surprisingly malleable public preferences for climate adaptation in forests
Bibliographic record
Abstract
Abstract Researchers and policy-makers often assume that public preferences for climate change adaptation are positive and stable compared to those of mitigation. However, public judgments about adaptation in natural resource sectors (like forestry) require that people make difficult, value-laden and uncertain trade-offs across complex social-ecological systems. The deliberative methods (e.g. focus groups and in-depth interviews) that are typically used to explore the malleability of these judgments may underestimate the level of preference malleability in broader publics by encouraging participants to rationalize their choices in relation to their own knowledge, values and beliefs, as well as those of others. Here, we use a public survey ( N = 1926) from British Columbia, Canada—where forestry is economically, environmentally and culturally vital—to investigate the malleability of public preferences for genomics-based assisted migration (AM) for climate change adaptation in forests. Following an initial judgment, respondents are given new information about AM’s potential implementation and impacts—simple messages similar to those that they might encounter through traditional and social media. The results show that respondents’ initial judgments are surprisingly malleable, and prone to large bi-directional shifts across all message types. The magnitude of this malleability is related to the degree of the proposed intervention, the type of message, and individuals’ demographic and psychographic characteristics. These results suggest that high levels of initial public support may be illusory, and that more attention should be paid to the potential for malleability, controversy and contradiction as adaptation policies are developed and implemented. Process-based arguments related to transparent, evidence-based and adaptive governance may be more influential than risk-based arguments related to climate change and economic impacts.
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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 | 0.000 |
| 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) | 0.001 | 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".