Make the Environment Great Again: Extending Past-focused Environmental Comparisons for Conservatives
Bibliographic record
Abstract
Conservatives are typically less pro-environmental than liberals. Promisingly, Baldwin and Lammers (2016) suggested conservatives may be swayed by pro-environmental messages promoting a return to a pristine past environment, instead of promoting avoidance of a degraded future environment. This study aimed to replicate and extend these findings by including nuanced political measures (SDO and RWA), and a third temporal comparison that focused simultaneously on the past and future, for wider political appeal. Participants (N = 563) viewed pro-environmental messages, images, and charities that were focused on either the past, the future, or both (combined). Unlike previous research, none of the conditions, impacted participants’ general environmental attitudes. Although, supporting Baldwin and Lammers (2016), individuals higher in RWA (typically more conservative) preferred pastfocused appeals and donated more to past-focused charities. Encouragingly, the combined condition was often just as, or more appealing than the other conditions to participants across the political spectrum.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".