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Record W4297459903 · doi:10.1177/10598405221127694

California School Staff Reports of Seeing Students Vaping at School and Disciplinary Actions

2022· article· en· W4297459903 on OpenAlex
Adam G. Cole, Brianna A. Lienemann, Joanna Sun, Jacqueline Chang, Shu‐Hong Zhu

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of School Nursing · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsOntario Tech University
FundersCalifornia Department of Education
KeywordsPunitive damagesDisciplineClass (philosophy)PsychologyMathematics educationMedical educationPedagogyMedicineSociologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.074
GPT teacher head0.427
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it