Mobilizing <scp>C</scp> anada to Promote Healthy Relationships and Prevent Bullying among Children and Youth
Bibliographic record
Abstract
Abstract In this chapter, we describe the evolution, mechanisms, and impact of PREVNet (Promoting Relationships and Eliminating Violence Network) in Canada. PREVNet is a national network of researchers, youth‐serving organizations, governments, and corporations working together to prevent bullying and promote healthy relationships for Canadian children and youth. PREVNet has been funded since 2006 by Canada's Networks of Centres of Excellence research program. Through PREVNet's partnerships, we have been engaged in a societal intervention by cocreating tools and resources to enhance the practices of those involved in the lives of children and youth across the country. We have developed several mechanisms to drive our efforts to foster wide‐scale social change, including four strategy pillars for knowledge mobilization, working groups to cocreate tools and resources, and broader strategies to engage multiple sectors within the country to focus on these important issues related to child and youth well‐being.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".