To dissent and protect: Stronger collective identification increases willingness to dissent when group norms evoke collective angst
Bibliographic record
Abstract
Research has shown that collective angst (i.e., concern for a group’s future vitality) triggers ingroup protective responses. The current studies examined whether group members seek to protect their group by dissenting from collective angst-inducing group norms. We hypothesized that strong (vs. weak) identifiers holding non-normative opinions would be more willing to dissent, but only when the normative opinion elicited collective angst. In Study 1, as predicted, strongly (vs. weakly) identified Republicans who held non-normative opinions about Obamacare were more willing to dissent, but only when collective angst was high. In Study 2, we manipulated rather than measured collective angst and examined a different political issue: the deployment of American ground troops to fight terrorism overseas. We observed the same pattern of dissent detected in Study 1. This research contributes to current understandings of dissent in groups and the motivating power of collective angst.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".