Bibliographic record
Abstract
The purpose of the study was to determine if the likelihood of middle school students engaging in physical or verbal bullying behaviors (specifically, breaking other people's things, trying to hurt or bother people, teasing other students, fighting with other students, and talking back to teachers) can be predicted by examining personal and school related factors, such as belief in pro-social norms, level of social integration, commitment to school, attachment to school, peer drug modeling, attitudes against substance use, self esteem, positive peer modeling, age, grade and gender. -- The data for this study was archival, having been originally collected by the researcher in 2004 to assess the impact of the Drug Abuse Resistance Education (D.A.R.E.) program in a rural Newfoundland and Labrador school. Logistic Regression Analysis was used to analyze the responses of 107 students in grades six to eight on the You and Your School questionnaire. -- The current study indicates that both physical and verbal bullying is influenced by gender and age. Self-esteem was also revealed as an important factor as were level of social integration, positive peer support and commitment to school. Implications for these findings are discussed in the context of creating a positive school environment. Limitations and recommendations are also 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".