Understanding the Dimensions of Climate Change Misinformation
Bibliographic record
Abstract
Climate change misinformation (CCM) is emerging as one of the most pressing barriers to climate action. Referring to false or inaccurate information about climate change, CCM threatens to cast confusion on both the severity and existence of climate change. As CCM has permeated into mainstream news and social media platforms, it can now reach larger audiences and decrease support for climate change mitigation practices and policies. To combat CCM effectively, more work is needed to understand it as one unified concept. This major research paper focuses on filling this gap by identifying the dimensions of CCM through an inductive content analysis of peer-reviewed literature. Utilizing an inductive approach, five overall dimensions of CCM were synthesized: attributes, psychology, politics, disinformation, and responses. These dimensions establish the necessary foundation to understand CCM as one concept, increase global resiliency to CCM, and develop strategies that focus on eliminating CCM in the future.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".