Choices We Make in Times of Crisis
Bibliographic record
Abstract
We present a new framework that allows understanding those we deem irrational in the climate debate. Realizing if the issue is one of information, beliefs, values or means opens the door for more constructive dialogue. Decision-makers diverge in their responses to the urgent need for action on climate and biodiversity. Action gaps are fueled by the apparent inability of decision-makers to respond efficiently to the mounting threats described by scientists—and increasingly recognized by society. Surprisingly, with the growing evidence and the accumulation of firsthand experiences of the impacts of environment crises, the gap is not only a problem of conflicting values or beliefs but also a problem of inefficient strategies. Bridging the gap and tackling the growing polarization within society calls for decision-makers to engage with the full complexity of the issues the world is facing. We propose a framework characterizing five archetypes of decision-makers to help us out of the current impasse by better understanding the behavior of others. Dealing with the complexity of environmental threats requires decision-makers to question their understanding of who wins and who loses, and how others make decisions. This requires that decision-makers acknowledge complexity, embrace uncertainty, and avoid falling back on simplistic cognitive models. Understanding the complexity of the issue and how people make decisions is key to having a fighting chance of solving the climate crisis.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| 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".