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.
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.
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 | 0.007 | 0.023 |
| 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 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".