Investigating the motivations underlying harmful social behaviors and the motivational nature of social norms
Bibliographic record
Abstract
Abstract The present research applies a self‐determination theory framework to capture the broad spectrum of reasons why individuals engage in harmful normative behaviors. This correlational study ( N = 242) focused on harmful behaviors that were either supported by one's in‐group or not. Participants whose in‐group encourages them to engage in a harmful behavior reported stronger motivation, both self‐determined and non‐self‐determined. Perceiving strong in‐group norms in favor of these behaviors was associated positively with the non‐self‐determined motivation pertaining to introjected regulation. The more participants agreed with an in‐group norm in favor of a harmful behavior, the stronger their self‐determination for engaging in this behavior. Results are discussed in light of self‐determination theory, normative models of social influence, and intergroup theories.
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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 | 0.002 | 0.000 |
| 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 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".