Kindness is Contagious: Standing up Against Bullying
Bibliographic record
Abstract
EAPO in partnership with Sheridan Centre for Elder Research, and the Bob Rumball Canadian Centre of Excellence for the Deaf are undertaking a Senior’s Anti-Bullying Implementation & Evaluation Project to better understand senior to senior bullying in Ontario, and support the development of best practices and strategies. While targeting bullying behaviour, our focus is on nurturing kindness and community building. Throughout this project, we have learned that you cannot tackle the issue of bullying without encouraging kindness and community. Many older adults do not know they are engaging in bullying behaviour until they see the impact on those around them. In this webinar we learned the “lessons” so far during the implementation and evaluation of the Senior’s Anti-bullying Project and what initiatives are coming up in the project’s future. An overview of the Solutions: Everybody’s Business Senior to Senior Anti-Bullying Toolkit, how it is being used in community settings as well as updates and additions being made to the toolkit based on engagement and input from seniors.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".