Doing the right thing even if you might fail: Does moral obligation interact with collective efficacy to predict environmental activism?
Bibliographic record
Abstract
Climate change poses a significant challenge, and despite growing calls from activists around the world, those in power have been slow to act.Plausibly, many would-be environmental activists may experience low collective efficacy, which can undermine collective action.A growing body of work suggests moral obligation is an important predictor of collective action.I tested the hypothesis that low collective efficacy undermines motivation to engage in collective action to a lesser degree for individuals who are high in moral obligation compared to those who are low in moral obligation.Study 1 examined qualitative interviews with 11 environmental activists to see how they discuss moral obligation and efficacy when talking about their activism.The majority of activists spoke of climate change as a moral issue and all activists who expressed low efficacy indicated moral motivations for their activism.Study 2, a secondary analysis on two correlational samples, provided some evidence of an interaction in a sample of undergraduate students (n=368), but not in a representative Canadian sample (n=1029).Study 3, a correlational study with an undergraduate student sample (n=428), showed no evidence of an interaction.Finally, Study 4 was an experiment (n=405); however, the experimental conditions failed to manipulate moral obligation and collective efficacy.Supplementary correlational tests once again provided no evidence of an interaction.Across all three quantitative studies, moral obligation was strongly associated with environmental activism even when controlling for collective efficacy.Thus, although the interaction hypothesis was not supported, these findings still provide evidence that moral obligation is an important predictor of environmental activism and deserves more attention.Those interested in inspiring environmental activism, such as activists and policymakers, need to focus not only on efficacy but also on the moral beliefs about and moral obligation toward climate change.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".