Using decision pathway surveys to inform climate engineering policy choices
Bibliographic record
Abstract
Over the coming decades citizens living in North America and Europe will be asked about a variety of new technological and behavioral initiatives intended to mitigate the worst impacts of climate change. A common approach to public input has been surveys whereby respondents' attitudes about climate change are explained by individuals' demographic background, values, and beliefs. In parallel, recent deliberative research seeks to more fully address the complex value tradeoffs linked to novel technologies and difficult ethical questions that characterize leading climate mitigation alternatives. New methods such as decision pathway surveys may offer important insights for policy makers by capturing much of the depth and reasoning of small-group deliberations while meeting standard survey goals including large-sample stakeholder engagement. Pathway surveys also can help participants to deepen their factual knowledge base and arrive at a more complete understanding of their own values as they apply to proposed policy alternatives. The pathway results indicate more fully the conditional and context-specific nature of support for several "upstream" climate interventions, including solar radiation management techniques and carbon dioxide removal technologies.
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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 | 0.026 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".