Global Climate Change Risk and Mitigation Perceptions: A Comparison of Nine Countries
Bibliographic record
Abstract
<p><span lang="EN-US">To broaden our understanding of global climate change (GCC), this article presents results from an ongoing longitudinal research project that investigates public GCC risk perceptions in nine countries focusing on different perceptions important in policy formulation. A key goal of the study is to understand which nations express similar or different viewpoints with respect to explanatory factors such as threat perceptions, hazard experience, socio-demographics, knowledge of climate change, and other factors found in the environmental hazards literature. Despite many variances in GCC perceptions among the surveyed national populations, the analysis shows that some differences are marginal, while others allow the grouping of countries based on different perception factors. Survey results reveal a high degree of uncertainty with regards to climate change dimensions including risk, science, knowledge, and policy approaches to mitigate GCC.</span></p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".