Threat on campus: a study of select Ontario post-secondary threat assessment teams' experience with threat assessment policy
Bibliographic record
Abstract
In Canada, there has been a total of 12 school shootings across the country over the past 40 years, four of which occurred in higher education; University of Alberta, Dawson College, Concordia University, and L’ecole Polytechnique (The Canadian Press, 2016; Payne, 2006; Rogerson, 2018; Watt, 2017). The safety of students is no longer considered to rest with one area of the campus community, such as campus security, and is instead considered to be a “we responsibility” (Mohandie, 2014, p. 131), involving administrators, parents, peers, and faculty reporting concerning behaviors to the campus team responsible for the evaluation of student dangerousness. These campus teams, commonly referred to as threat assessment teams (Meloy et al., 2014; Sokolow et al., 2016) determine the level of targeted violence risk posed by a student towards self or others (ATAP, 2006; Borum et al., 1999; Meloy et al., 2014). Limited research exists with a focus on how institutional threat assessment policies are developed at the post-secondary level. This study shed light on threat assessment in Canada through the utilization of policy in a uniquely Canadian education system, specifically within the province of Ontario. It is evident that different post-secondary institutions are choosing to support different models of threat assessment management (TAM). These different models included all aspects of violence prevention from team member selection, to policy and procedure development, to the tool used to determine level of risk.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".