The 2018 Toronto Van Attack: Understanding the Disaster by Looking at Vulnerability, Tactics, and Motives
Bibliographic record
Abstract
On April 23, 2018, an attacker drove a van along Toronto’s Yonge Street and its sidewalks, purposely colliding with pedestrians. Ten persons were killed, and 16 were injured. Emergency managers in Canada’s urban centres will occasionally face socially mediated disasters originating from anti-social actions taken by ideologically motivated, violent extremists. The Toronto Van attack was a disturbing rapid sequence mass murder, which was especially depraved due to its sheer levels of violence and specific targeting of women. In an attempt to comprehend the disaster, this paper explores issues of vulnerability, tactics, and motives related to the vehicular ramming attack. It is suggested that the pervasive threat of gender-driven violence needs to be recognized, crime prevention through environmental design and counter terrorism-based soft target hardening strategies can work to reduce risks, failure of imagination leading to disregard of criminal and terrorist threats should be avoided, and any security measures to defend pedestrians should be commensurate with the actual risk present.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".