Seniors’ Anti-Bullying Project: Authentic Student Engagement (Presentation)
Bibliographic record
Abstract
People of all ages can be bullied, yet data on bullying between older adults is lacking. To better understand and support the development and implementation of best practices for targeting bullying between older adults, the Seniors’ Anti- Bullying project was established. It began more than four years ago with a large-scale survey of older adults in Ontario, resulting in an anti-bullying toolkit that will now be implemented and evaluated in eight Seniors’ Residences. When working on such a long-term project, with many partners, implementation sites, as well as a different cohorts of student researchers, it is essential to ensure that the students’ experience of the project is authentic and their contribution is genuine. To do this, ongoing training and mentorship is one key component of the project itself. This has resulted not only in strong student engagement, but also invaluable contributions on the part of the students. That said, as the project evolves, so must training and mentorship. As such, this presentation will not only discuss how students have been supported and involved to date, but also how from a student researcher perspective, we can grow
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.009 |
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".