Trust in Climate Scientists and Political Identity: A 26-Country Secondary Data Analysis
Bibliographic record
Abstract
Preprint: https://doi.org/10.31219/osf.io/dvczr The current research aims to examine the political antecedents of trust in climate scientists in a large, multi-country sample. We do so by analyzing secondary data from Većkalov, Geiger et al. (2023), collected across a 27-country convenience sample spanning six continents. We hypothesize that political identity and trust in climate scientists are related linearly. We expect that left-leaning individuals trust climate scientists more than right-leaning individuals. However, in English-speaking countries (the U.S., Canada, Australia, and the UK), political cultures seem to have evolved in a way that prompts citizens to appraise climate scientists through the lens of their political identities (Czarnek et al., 2020; Funk et al., 2020; Hornsey et al., 2018; Rutjens et al., 2022; Smith & Mayer, 2019). Consequently, we anticipate that this association will be most pronounced in these countries. We will also test the moderation effect of education on the predictive power of political identity on trust in climate scientists. Finally, we control for the effects of age, gender, education, and GDP per capita, which, as indicated by prior research, seems to contribute to variations in trust in climate scientists.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".