Social comfort zones for transformative conservation decisions in a changing climate
Bibliographic record
Abstract
Novel management interventions intended to mitigate the impacts of climate change on biodiversity are increasingly being considered by scientists and practitioners. However, resistance to more transformative interventions remains common across both specialist and lay communities and is generally assumed to be strongly entrenched. We used a decision-pathways survey of the public in Canada and the United States (n = 1490) to test two propositions relating to climate-motivated interventions for conservation: most public groups are uncomfortable with interventionist options for conserving biodiversity and given the strong values basis for preferences regarding biodiversity and natural systems more broadly, people are unlikely to change their minds. Our pathways design tested and retested levels of comfort with interventions for forest ecosystems at three different points in the survey. Comfort was reexamined given different nudges (including new information from trusted experts) and in reference to a particular species (bristlecone pine [Pinus longaeva]). In contrast with expectations of public unease, baseline levels of public comfort with climate interventions in forests was moderately high (46% comfortable) and increased further when respondents were given new information and the opportunity to change their choice after consideration of a particular species. People who were initially comfortable with interventions tended to remain so (79%), whereas 42% of those who were initially uncomfortable and 40% of those who were uncertain shifted to comfortable by the end of the survey. In short and across questions, comfort levels with interventions were high, and where discomfort or uncertainty existed, such positions did not appear to be strongly held. We argue that a new decision logic, one based on anthropogenic responsibility, is beginning to replace a default reluctance to intervene with nature.
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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) | 0.000 | 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".