The Motivational Dynamics of Dissent Decisions
Bibliographic record
Abstract
We propose that dissent decisions involve a tension between shorter term group stability goals and longer term group change goals. Strongly identified members may be animated by either goal, and their behavior with respect to group norms is influenced by which is currently dominant. In two experiments, we manipulated construal level, a factor that affects goal selection, such that people are more likely to make decisions that further long-term goals at high (vs. low) construal level. As predicted, at high construal level, strong identifiers were more willing to dissent from group norms than weak identifiers; at low construal level, strong identifiers were equally or more conformist. These findings advance understanding of the motivational dynamics of dissent decisions and speak to the nature of depersonalization/self-categorization in groups. Identified members retained individual agency and exercised their own judgment regarding group norms, choosing to deviate when they perceived it to be in the group’s interest.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".