School violence : the role of families, communities, educators and school counsellors
Bibliographic record
Abstract
School violence is defined as anything that makes a student afraid to come to, and stay at school (Macdonald, 1996). Media reports of the suicides of children who were the victims of bullying, school shooting rampages in Littleton, Colorado and Tabor, Alberta, and the beating death of Rena Virk have brought the problem of school violence to the forefront. What follows is a series of three papers that attempt to inform school counsellors, and others working in the school system, about school violence and the actions that may be taken to ensure that every student is provided a safe learning environment. Paper one describes the current views held about youth and school violence. This includes a discussion of the forms, perpetrators, incidence, and risk factors for violence. Paper two concentrates on the prevention and intervention measures that can be implemented at the family, community and school levels. Finally, paper three focuses on the role the guidance counsellor can play in the school environment to help make it a safer place for students to learn.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".