California School Staff Reports of Seeing Students Vaping at School and Disciplinary Actions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Youth vaping is a concern and schools may use many approaches to discipline students caught vaping at school. This study identified the prevalence of school staff seeing vaping in schools and the measures used to discipline students. A state-wide sample of 7,938 staff from 255 middle and high schools reported whether they saw any students vaping at school in the last 30 days, whether they have caught any students vaping during class in the last semester, and what happened after catching a student vaping in class. Open-text responses were coded and themes were identified related to disciplinary approaches. 31.9% of staff reported seeing students vaping at school, and 11.9% of teachers reported catching a student vaping during class. Teachers described four categories of disciplinary approaches after catching students vaping in class: no consequences, punitive approaches, restorative approaches, and mixed approaches. Additional support is necessary to help schools address student vaping.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it