Political Common Ground on Preserving Nature: Environmental Motives Across the Political Spectrum
Bibliographic record
Abstract
Environmental issues are becoming increasingly politically polarized, making common ground essential. This research investigated the political common ground of environmental motives—the reasons why nature is worth preserving. Natural language processing of liberals’ and conservatives’ open text responses (Study 1: N = 1,544) identified 12 central motives. Political common ground was shared on the most cited motives: Human survival, moral obligations to future generations, and appreciation for nature’s beauty. Political differences emerged on motives related to climate change risks and religious stewardship. Study 2 ( N = 796) replicated these findings using a validated self-report questionnaire based on participant responses in Study 1. Factor analysis indicated motives belonged to four categories: Responsibility to nature, instrumental benefits, childhood experiences, and religious stewardship. These motives explained substantial variance in environmental attitudes and partially accounted for political differences in attitudes. The studies used mixed methods and direct/conceptual replication to build confidence in key findings and longstanding theoretical frameworks.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".