Bibliographic record
Abstract
Bullying is an issue that many students within a school environment experience on a daily basis. Teachers are frequently present when these bullying instances arise. This study focuses on two Ontario secondary teachers’ beliefs about how their responses to bullying affect the school dynamics of bullying. The study also addresses the teachers’ philosophies about bullying, and their professional performance when responding to bullying. A qualitative study is included, including one semi-structured interview with each participant. The participants were teaching at high schools located within the Greater Toronto Area (GTA) when they were interviewed. The study reveals a discrepancy between common victim characteristics, making it difficult for teachers to notice bullying. Thus, teachers are frequently unaware that bullying is taking place in their classes. The study indicates the challenges that teachers endure when influencing the school dynamics of bullying, as consequences for the actions of bullying are dealt with by administration and are out of the control of teachers. The study also addresses documents and policies implemented by the Ontario Ministry of Education about how to effectively reduce and respond to school bullying. The discussion gives recommendations for teachers on how to effectively address bullying, and examines the importance of integrating bullying into the curriculum.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".