Pressuring Others: Examining the Motivations Behind Deviant Instigation and the Strategies That Accompany Them
Bibliographic record
Abstract
Research on the motivations behind peer pressure and deviance has been close to non-existent. This dissertation presents a mixed methods study that was conducted exploring the motivations and strategies behind deviant instigation. In phase one of this research, semi-structured interviews (n=40) were conducted with people who have encouraged others to either steal or use alcohol or drugs in a context that was against the law. Phase two of this research tested the main motivations and strategies resulting from phase one in an online survey (n=214) with people residing in Canada and the United States alongside a wide variety of acts and other possible motivations and strategies. Although multiple motivations and strategies were used by people pressuring others, a few consistently emerged. Specifically, the most common motivations for why people encouraged others to break the law included seeking an improved experience for themselves in the moment and wanting to help the other person. The most common strategy for enacting this pressure was providing reassurance (e.g., “You’ll be fine!”). This dissertation will discuss this research in depth as well future directions and implications.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".