The “Danger” of Consensus Messaging: Or, Why to Shift From Skeptic-First to Migration-First Approaches
Bibliographic record
Abstract
Consensus messaging is a climate change communication strategy emphasizing the fact of scientific consensus on anthropogenic global warming (AGW). Its proponents encourage scientists, journalists and educators to transmit consensus messages in hopes of improving public climate literacy. Critics of this approach question its methodology for determining consensus and its effectiveness as a strategy for improving public understanding and policymaking. I review these debates to determine what is at stake in disagreements over consensus messaging and suggest that issues of climate change danger are addressed too narrowly when the expectations, style and categories of consensus messaging are dominant. I recommend that ‘migration-first’ approaches displace the priority of ‘skeptic-first’ approaches to climate change communication, and that scholars begin asking what is owed to those most affected by climate change danger.
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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 | 0.098 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.015 | 0.036 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".