The Evolution of Estimated Time of Arrival: The City of Toronto’s CVE Program
Bibliographic record
Abstract
The Countering Violent Extremism (CVE) field is evolving in Canada, with CVE teams offering psychosocial disengagement interventions in the provinces of Alberta, British Columbia, Manitoba, Ontario, Saskatchewan and Quebec. This article is written as a case study to detail the evolution of the city of Toronto’s CVE program, called Estimated Time of Arrival (ETA), housed in the community mental health centre Yorktown Family Services (“Yorktown”). Toronto – the largest Canadian city, provincial capital of Ontario, and one of the most multicultural cities in the world – has seen several high-profile cases of violent extremism and terrorism over the past few years. For example, there were two “Incel” attacks that together killed eleven people, including nine women, in 2018 and 2020. In 2022, hate crime occurrences reported to the Toronto Police Service were 74% higher than pre-pandemic levels, and 40% higher than the 10-year average. Clearly, there was a need for a structured and multi-sectoral response which led to the inception of ETA in 2020. Against this backdrop, this paper outlines ETA’s program components and operational design. Various data points such as client age range, ideological affiliation, and services rendered are provided to demonstrate trends for the period of April 2022 to March 2023. As this paper will demonstrate, ETA’s services are grounded in engagement, outreach, case management (multi-agency service delivery), psychotherapy, religious counselling, peer support and forensic consultation, which is reflected in the evolving CVE literature and evidence-base.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".