Beyond choice architecture: a building code for structuring climate risk management decisions
Bibliographic record
Abstract
Abstract Although the need for urgent climate change action is clear, insights about how to make better climate risk management decisions are limited. While significant attention from behavioral researchers has focused on choice architecture, we argue that many of the contexts for addressing climate risks require increased attention to the needs of a deliberative and dynamic choice environment. A key facet of this kind of decision is the need for decision-makers and stakeholders to identify and balance conflicting economic, social and environmental objectives. This recognition of difficult, context-specific trade-offs highlights the need for structuring the decision-making process so that objectives are clearly articulated and prioritized. Equally, policy analyses and deliberations must effectively link priorities with climate risk management options. This restructuring of decision-making about climate change calls for more than a nudge. Scientific and technical efforts must be redirected to help stakeholders and decision-makers better understand the diverse implications of climate change management alternatives and to become better equipped to take actions commensurate with the urgency of the problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".