Machine learning identifies key individual and nation-level factors predicting climate-relevant beliefs and behaviors
Bibliographic record
Abstract
We used machine learning to extract unique insights from a recent dataset across 55 countries (N = 4,635). The current analysis identifies the most important individual-level and nation-level predictors of climate-friendly beliefs and behaviors. Interpretable machine learning ranked 19 variables by importance for predicting climate change belief, policy support, willingness to share climate change information on social media, and a pro-environmental behavior task. We find notable differences in explained variance per outcome (e.g. 57% for climate change belief vs 10% for the pro-environmental behavior). Most predictors show divergent patterns, predicting some but not all outcomes or even having opposite effects. However, four consistent predictors were identified including Human Development Index, environmental identity, trust in climate science, and internal environmental motivation, highlighting the importance of fostering environmental identities, safeguarding trust in science, and better understanding the impact of such individual and nation-level factors in this space
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".