Defending defined: Exploring how youth defending against peer victimization
Bibliographic record
Abstract
The current dissertation comprises three research studies that examined how youth defend their peers from victimization, as well as the social-emotional correlates of these defending behaviors. Theories of prosocial behavior, social-information processing, and adolescent socialization were applied to understand the multidimensional nature of peer defending behaviors across studies. Study 1 focused on the development and validation of the Defending Behaviors Scale (DBS) in a sample of early adolescents (n = 572). Results supported a four-factor model consisting of comforting, reporting, solution-focused, and aggressive defending. Most youth reporting using a variety of defending behaviors, with girls being more likely to use comforting and reporting and boys being more likely to use aggressive defending. Studies 2 and 3 used the DBS to examine the social-emotional predictors and outcomes of these defending behaviors. Study 2 (n = 120) employed a novel virtual-reality paradigm to examine defending behaviors as they unfold in real- time. The central finding from this study indicated that both anger and empathy interact to predict different defending behaviors. Lastly, study 3 (n = 334) used a longitudinal social network design to examine the social risks and rewards of defending behaviors in the peer group. Results demonstrated peer socialization effects for comforting, reporting, and solution-focused defending behaviors. Each defending behavior was reinforced in gender-normative ways by the peer group in terms of subsequent changes in victimization and popularity. Through the integration of theoretical perspectives and application of novel research methods, this dissertation describes the intrapersonal and interpersonal factors that influence how youth defend against peer victimization. Findings from this research can inform the development of bullying prevention and intervention efforts that aim to increase peer defending among early adolescents.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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; 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".