Living Well Together: Confronting Bullying in Seniors’ Communities
Bibliographic record
Abstract
Bullying is not confined to schoolyards. It can rear its ugly head in any community, including senior living spaces. It is a pressing issue in community housing that demands attention. Discover how researchers from Sheridan College and a senior’s housing provider joined forces to combat tenant-on-tenant bullying. Glean insights from Sheridan College’s anti-bullying toolkit, rooted in Ontario-based research and designed to equip tenants with the skills to stand up and speak out. Together, we will cover: How the Anti-Bullying Toolkit supports seniors and practical strategies to foster a bully-free community Real-life success stories from the first seniors housing provider partnership and the impact of the Train the Trainer model Future possibilities for senior anti-bullying initiatives and how you can get involved From strategies for assertive communication and prevention, to real-life success stories, this session offers practical insights for fostering a culture of respect at any age!
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".