Bibliographic record
Abstract
Central to the definition of bullying is the abuse of power. Power relationships are inherent in social groups, by virtue of differing size, strength, age, socioeconomic status (SES), or social connectedness of individuals. Beyond the influence of these individual characteristics, environmental factors have the potential to affect the power dynamic in bullying and victimization, but are a relatively unexplored research area. In particular, we know little about how the community-level factors relate to bullying in the adolescent population. The present study is guided by a social ecological perspective to gain a deeper understanding of the individual and built community characteristics (e.g., parks, recreational spaces, buildings that facilitate social interaction and community connectedness) that contribute to the power dynamics related to victimization or bullying in traditional and electronic contexts. Data were collected from 17,777 students in Grades 6 to 10 as part of the 2009/2010 Health Behavior in School-aged Children (HBSC) Survey, from Geographical Information Systems (GIS) data, and from 2006 Canadian Census data. Two nested models were run using HLM with age, gender, ethnicity, SES, social inclusion factors (e.g., collective efficacy), and community resource factors (e.g., access to recreation) as predictors. Characteristics of individuals that placed them at a power disadvantage (being younger, female, and having low SES) were linked to higher rates of victimization. Lower individual collective efficacy was also associated with higher rates of traditional and electronic victimization. Community recreational opportunities were associated with decreased victimization in both contexts. The relative importance of individual and built environmental factors and conceptualization of bullying interactions within a social ecology model are discussed.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".