Money and age in schools: Bullying and power imbalances
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it