Wrinkles in Time and Drops in the Bucket: Circumventing Temporal and Social Barriers to Pro-Environmental Behavior
Bibliographic record
Abstract
Human engagement in pro-environmental action is necessary for mitigating the effects of climate change. However, psychological barriers, such as feeling that the problem is distant in time and that any personal actions would only be a “drop in the bucket,” reduce people’s motivation to engage in pro-environmental behaviors that are essential for the future of the planet but that incur short-term personal costs. In the present study, we drew on theory and research regarding the subjective experience of temporal distance and the effects of social norms on action. We used an experimental methodology in which we presented scientifically predicted outcomes of climate change expected around the year 2100, then manipulated the degree to which these future consequences felt proximal or distant. We also altered whether people perceived pro-environmental action to be normative in society, reasoning that people would be more motivated to take on a subjectively looming threat if they believed they were part of a collective who were also taking action. Results indicated that after considering far-off, large-scale climate outcomes, neither subjective proximity nor social norms were sufficient in isolation to motivate behavior, but in combination, they effectively increased pro-environmental intentions and behavior. Those who were induced to feel that these objectively distant future outcomes were subjectively imminent, and who were also led to believe that pro-environmental behavior was normative, reported more intentions to engage in environmentally responsible behavior, and actually reported more sustainable behaviors in the weeks following the study.
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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 | 0.001 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".