Bibliographic record
Abstract
Bullying is often overlooked on school buses, even though many students experience bus rides every school day. Since bus-drivers are often tasked as the lone adult supervisors of these children, their observations and experiences have the potential to be useful in understanding and ultimately preventing bullying in this context. This research will gather data from bus-drivers about their level of preparedness in addressing bullying on the school-bus by examining the kinds of bullying bus-drivers witness on their bus and how frequently; what strategies bus-drivers use to address bullying; how bus-drivers are supported in addressing bullying; and how bus-drivers perceive their own preparedness for addressing bullying. Data will be collected using a sequential mixed-methods approach. During the first phase, an online survey will be distributed to a sample of 130 participants randomly selected from a population of 650 bus-drivers in an Ontario region. Numeric responses will be analysed using descriptive and inferential statistics, while thematic analysis will be employed with text responses. The second phase will involve semi-structured interviews of eight bus-drivers to explore their perceptions and experiences in more depth. The findings from this study will contribute to understanding bullying in school-bus environments, as experienced by bus-drivers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".