Imagining a Sustainable World: Measuring Cognitive Alternatives to the Environmental Status Quo
Bibliographic record
Abstract
We build on social identity models of environmental collective action by considering the role ofpeople’s access to cognitive alternatives to the environmental status quo. We developed a new measure of cognitive alternatives to the environmental status quo, and examined its ability to predict environmental activist identification and willingness to engage in environmental activism. In Study 1 (N = 386), we developed the initial scale, and found evidence for its reliability and validity. The ability to imagine cognitive alternatives was associated with other relevant social identity and environmental variables including perceived legitimacy of the current environmental status quo, pro-environmental consumer and activist behavior, and beliefs in anthropogenic climate change. In Study 2 (N = 393), we confirmed the factor structure of the scale and found that it was a strong predictor of environmental activist identification, explaining variance beyond extensive control variables including identification with nature. It also explained additional variance in willingness to engage in activist behavior beyond even environmental activist identification. Our results suggest that the ability to imagine cognitive alternatives to the environmental status quo might have important implications for whether people engage in pro-environmental collection action to mitigate climate-change and other environmental problems.
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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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".