Socioeconomic Inequality in Exposure to Bullying During Adolescence: A Comparative, Cross-Sectional, Multilevel Study in 35 Countries
Bibliographic record
Abstract
OBJECTIVES: We examined the socioeconomic distribution of adolescent exposure to bullying internationally and documented the contribution of the macroeconomic environment. METHODS: We used an international survey of 162,305 students aged 11, 13, and 15 years from nationally representative samples of 5998 schools in 35 countries in Europe and North America for the 2001-2002 school year. The survey used standardized measures of exposure to bullying and socioeconomic affluence. RESULTS: Adolescents from families of low affluence reported higher prevalence of being victims of bullying (odds ratio [OR] = 1.13; 95% confidence interval [CI] = 1.10, 1.16). International differences in prevalence of exposure to bullying were not associated with the economic level of the country (as measured by gross national income) or the school, but wide disparities in affluence at a school and large economic inequality (as measured by the Gini coefficient) at the national level were associated with an increased prevalence of exposure to bullying. CONCLUSIONS: There is socioeconomic inequality in exposure to bullying among adolescents, leaving children of greater socioeconomic disadvantage at higher risk of victimization. Adolescents who attend schools and live in countries where socioeconomic differences are larger are at higher risk of being bullied.
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.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".