Lessons learned from dual site formative evaluations of Countering violent extremism (CVE) programming co-led by Canadian police
Bibliographic record
Abstract
Drawing on lessons learned from recently completed formative evaluations of police co-led CVE programming in Toronto, Ontario and Calgary, Alberta, this research aims to underscore the importance of, and provide technical guidance on, evaluation and reporting standards in the context of multi-agency CVE programming – which ultimately will help to facilitate the identification and replication of good practice. The results of the evaluative process highlight the need for greater articulation regarding intended program outcomes as well as program theorising regarding the underlying mechanisms that connect program activities and outputs with said intended outcomes. Both evaluations also demonstrated the importance of prioritising collaboration at both the evaluation-level and the program-level to facilitate successful and robust program implementation. As such, this study also yields findings that speak to the beneficial role that the evaluative process itself can play in facilitating the evolution of CVE programming.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".