Money and age in schools: Bullying and power imbalances
Bibliographic record
Abstract
School bullying continues to be a serious problem around the world. Thus, it seems crucial to clearly identify the risk factors associated with being a victim or a bully. The current study focused in particular on the role that age and socio-economic differences between classmates could play on bullying. Logistic and multilevel analyses were conducted using data from 53,316 5th and 9th grade students from a representative sample of public and private Colombian schools. Higher age and better family socio-economic conditions than classmates were risk factors associated with being a bully, while younger age and poorer socio-economic conditions than classmates were associated with being a victim of bullying. Coming from authoritarian families or violent neighborhoods, and supporting beliefs legitimizing aggression, were also associated with bullying and victimization. Empathy was negatively associated with being a bully, and in some cases positively associated with being a victim. The results highlight the need to take into account possible sources of power imbalances, such as age and socio-economic differences among classmates, when seeking to prevent bullying. In particular, interventions focused on peer group dynamics might contribute to avoid power imbalances or to prevent power imbalances from becoming power abuse. Aggr. Behav. 41:280-293, 2015. © 2014 Wiley Periodicals, Inc.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".