Youth Counter-Radicalization Program: A Practical Application of Domestic Security
Bibliographic record
Abstract
On September 18th, 2025, Ms. Lila Green presented Youth Counter-Radicalization Program: A Practical Application of Domestic Security. The presentation was followed by a question-and-answer period with questions from the audience and CASIS Vancouver executives. Ms. Green’s presentation focused on school-based, primary-prevention programming to address youth online radicalization, which she argues has now become an urgent requirement rather than an optional add-on. To meet this need, Ms. Green outlined how security-sector practitioners should co-deliver content with educators to close the persistent gap between high-quality materials and the students who need them, ensuring instruction that is both realistic and empathetic. She relied on evidence from a Vancouver School Board pilot she undertook at John Oliver Secondary which showed that this model works. The key findings demonstrated how students reported substantial knowledge gains and strong support for continuing and expanding the program. Building on this momentum, the program is now set to be scaled to new schools and districts, adding teacher professional development, and seeking long-term integration into curricula alongside parent- education components.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".