CHAPTER 1 Moral Disengagement: A Framework for Understanding Bullying Among Adolescents
Bibliographic record
Abstract
Bullying, a subcategory of aggressive beha-vior, is encountered regularly by children and adolescents in the context of schools worldwide (for an overview see Smith et al., 1999; Whitney and Smith, 1993). In Canada, self-report data indicate that 8 to 9 % of elementary school children are bullied frequently (i.e., once or more a week) and about 2 to 5 % of students bully others frequently (Bentley and Li, 1995; Charach, Pepler, and Ziegler, 1995). Among adolescents, at the secondary school level, rates are somewhat higher, with 10 to 11 % of students reporting that they are frequently victimized by peers, and another 8 to 11 % reporting that they frequently bully others (Vaillancourt and Hymel, 2001). Observational studies show that, although peers are present in most bullying situations (85 to 88%), they seldom intervene on behalf of victims (11 % to 25 % of the time) (Atlas and Pepler, 1998; Craig and Pepler, 1997) and many students just watch, while others even join in (O’Connell, Pepler, and Craig, 1999). Although bullying is a common experience for students around the world, it is a complex social problem that can have serious negative consequences for both bullies and victims (see Salmivalli, 1999; Smith and Brain, 2000). The negative effects of bullying are well documented, not only in terms of the psychological harm that is inflicted upon victims, but also in terms of the maladaptive outcomes for children who engage in bullying. Studies from countries around the globe tell us that bullying behavior predicts later criminality and delinquency (Olweus, 1991; Pulkkinen and Pitkanen, 1993) and is associated with both externalizing and internalizing diffi-
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".