Passion and moral disengagement: Different pathways to political activism
Bibliographic record
Abstract
OBJECTIVE: Four studies examined the relationship between motivational imbalance-the degree to which a goal dominates other goals-and political activism. METHOD: Based on the dualistic model of passion (Vallerand, 2015) and recent theorizing on violent extremism (Kruglanski, Jasko, Chernikova, Dugas, & Webber, 2017), we predicted that obsessive passion (OP), which facilitates alternative goal suppression, would increase support for violent political behaviors. In contrast, we predicted that harmonious passion (HP), which facilitates the integration of multiple goal pursuits, would increase support for peaceful political behaviors. RESULTS: Study 1a demonstrated that OP for environmentalism was positively associated with moral disengagement, which in turn predicted violent behaviors. HP was positively associated with peaceful behaviors. Political activism among Democrats yielded similar findings in Study 1b. Study 2 replicated Studies 1a-1b using an implicit measure of moral disengagement. Study 3 replicated Studies 1-2 by demonstrating that experimentally inducing a harmonious (vs. obsessive) passion mindset indirectly reduced violent behaviors through the attenuation of moral disengagement while directly promoting peaceful behaviors. Study 4 conceptually replicated Studies 1-3 by experimentally manipulating moral disengagement. CONCLUSIONS: These results offer insights into the workings of radicalization and suggest theory-driven methods of reducing political violence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".