"SCHOOL ADJUSTMENT OF TEENAGERS: THE RELATIONSHIP BETWEEN BULLYING, VICTIMIZATION AND RESILIENCE FACTORS"
Bibliographic record
Abstract
Bullying and victimization are among the most worrying problems that can undermine school climate.Worldwide, studies show that between 6% and 45% of students are involved in those kinds of behaviours, depending on countries and methodologies (Bowen et al., 2018).In addition to reaching a significant proportion of students, studies list a variety of short-and long-term consequences related to school violence such as various psychological difficulties (Hawker & Boulton, 2000), poor grades and school dropout (PISA, 2015).While school violence is widespread and devastating, some adolescents maintain positive adjustment throughout their schooling despite victimization.Resilience can take a variety of meanings, but it's mostly associated with the ability to maintain normal functioning despite adversity (Luthar et al., 2000).In this context, objectives of the study are: 1) to provide a global picture of bullying and victimization in high schools in Quebec (Canada); 2) to study the relationship between bullying, victimization, resilience factors and school adjustment.A total of 165 high school students completed a survey on bullying, victimization, resilience, and related topics.Results show that 23% of teenagers have adopted bullying behaviours in the past two months, while 31% reported being bullied.Furthermore, 44% of adolescents reported bullying behaviours at some point in their schooling and 61% reported being a victim.Hierarchical regression analysis shows that reported victimization and resilience factors account for 44% of the school adjustment variance, with resilience factors contributing more to the predictive model (26%) than reported victimization (18%).This study highlights the extent of violence in the school context and how resilience components can act as protective factors and maintain positive coping.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".