To dissent and protect: Stronger collective identification increases willingness to dissent when group norms evoke collective angst
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it