Experimentally elevating environmental cognitive alternatives: Effects on activist identification, willingness to act, and opposition to new fossil fuel projects
Bibliographic record
Abstract
According to social identity theory, people are more likely to demand and collectively work for social change if they can imagine a different set of social relations – “cognitive alternatives to the status quo”. In prior work, access to environmental cognitive alternatives (ECA; i.e., access to ideas about how the relationship between humans and the rest of nature could be more sustainable) correlated with environmental activist intentions and observed activist behaviour. In three preregistered experiments ( N = 3096), we expand this work to validate a manipulation of environmental cognitive alternatives and examine the causal effects of environmental cognitive alternatives on identification with and willingness to engage in environmental activism. Participants either imagined and wrote about a sustainable world or were in a control condition. In Study 1 and 2, the manipulation significantly increased scores on the Environmental Cognitive Alternatives Scale, and analyses supported the construct validity of the manipulation. The ECA manipulation increased identification with environmental activists (Study 1, 2, & 3) and willingness to engage in environmental activism (Study 1 & 3). In Study 3 we also tested the effects of the manipulation on opposition to new fossil fuel infrastructure and found that the ECA manipulation increased opposition. Further, the effect of the ECA manipulation on willingness to engage in environmental activism and on opposition to fossil fuel expansion projects was mediated by identification with environmental activists. These findings provide evidence that we developed a valid manipulation, and that environmental cognitive alternatives can generate support for pro-environmental social change. • We created and validated a manipulation of environmental cognitive alternatives. • The manipulation consisted of imagining and writing about a sustainable world. • The manipulation increased environmental activist identity and activist willingness. • The manipulation increased opposition to new fossil fuel projects. • Activist identity mediated the effects on activist willingness and project opposition.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".