Heterogeneity in climate change beliefs across New Zealand’s rural sector
Bibliographic record
Abstract
In this paper we present novel evidence about heterogeneity in climate beliefs using a large-scale survey of farmers, foresters, growers, and lifestyle block owners in New Zealand. Using a flexible, conditional-moments approach, we estimate the interpersonal dispersion in climate change beliefs conditional on individual characteristics, which provides a direct measure of the heterogeneity in beliefs about climate change. Our results show that women, younger respondents, farmers with less family farming history, higher educated respondents, and those respondents who are less trusting in social media are more likely to believe in climate change. Further, beliefs are more heterogeneous among males (young and old), the less educated, and those who trust social media. Our results offer new insights allowing governments and NGOs to design and communicate policies to reduce the heterogeneity in climate change beliefs, which should support the uptake of climate change actions.
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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 | 0.001 | 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".