The Protective Power of Behavioural Threat Assessment (& Management) (BTAM)
Bibliographic record
Abstract
On August 20th, 2020, the Canadian Association for Security and Intelligence Studies (CASIS) Vancouver hosted its fourth digital roundtable event of the year, The Protective Power of Behavioural Threat Assessment (& Management) (BTAM). The presentation was conducted by guest speaker Andrea Ringrose, Director of Campus Public Safety at Simon Fraser University, who is also on the Board of Directors at Canadian Association of Threat Assessment Professionals. Ringrose’s presentation gave an overview on behavioural threat assessment and management, and how public safety and caring for persons of concern are interconnected when assessing threats and risks. Subsequently, Ringrose answered questions submitted by the audience, which focused on the assessment of different offender types, the handling bias during the BTAM process, the role of artificial intelligence, and the possibility of echo chambers accelerating behaviour.
 APA Citation
 CASIS Vancouver. (2020). The protective power of behavioural threat assessment (& management) (BTAM). The Journal of Intelligence, Conflict, and Warfare, 3(2), 77-83. https://journals.lib.sfu.ca/index.php/jicw/article/view/2409/1816.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".