Explaining Normative Versus Nonnormative Action: The Role of Implicit Theories
Bibliographic record
Abstract
The current research investigates what motivates people to engage in normative versus nonnormative action. Prior research has shown that different emotions lead to different types of action. We argue that these differing emotions are determined by a more basic characteristic, namely, implicit theories about whether groups and the world in general can change. We hypothesized that incremental theories (beliefs that groups/the world can change) would predict normative action, and entity theories (beliefs that groups/the world cannot change) as well as group identification would predict nonnormative action. We conducted a pilot in the context of protests against a government plan to relocate Bedouin villages in Israel and a main study during the Israeli social protests of the middle class. Results revealed three distinct pathways to collective action. First, incremental theories about the world predicted hope, which predicted normative action. Second, incremental theories about groups and group identification predicted anger, which also predicted normative collective action. Lastly, entity theories about groups predicted nonnormative collective action through hatred, but only for participants who were highly identified with the group. In sum, people who believed in the possibility of change supported normative action, whereas those who believed change was not possible supported nonnormative action.
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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 | 0.000 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".