Bibliographic record
Abstract
Despite recognition that teacher-directed violence is a common phenomenon that is considered a “salient and concerning” (Wilson et al., 2011, p. 2354); it remains widely overlooked and understudied. Teacher-directed violence garners very limited attention internationally (Galand, Lecocq, & Philippot, 2007; Dzuka & Dalbert, 2007; Chen & Astor, 2008; Wilson, et al., 2011; Ozkilic & Kartal, 2012; Kauppi & Porhola, 2012) despite its broad impacts like those on stakeholder well-being, schools and school climate, teacher recruitment/retentions, and student academic and behavioural outcomes (Espelage, et al. 2013). This article reviews literature concerned with teacher-directed violence from 1983 through 2019. The literature derives publications from international contexts (North America, Europe, the Middle East, and Asia) exploring and comparing experiences of teacher-directed violence. The analysis of the studies examines teacher-directed violence from a socio-ecological model developed by McMahon et al. (2017), and results explore the implications of teacher-directed violence, perspectives on why teacher-directed violence occurs, preventative measures, as well as the identification of common types of violence teachers experience: (1) verbal behaviour, (2) non-verbal behaviours, (3) physical behaviour, (4) damage to personal property, and (5) technology related behaviours. This research has implications for researchers, teacher pre-service, professional development training, school administrators, community leaders, and policymakers.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".