Key Learnings from ‘Seniors of Canada’: A Community Project Aimed to Disrupt Ageism
Bibliographic record
Abstract
Abstract Visual representations of aging have historically relied upon binarized clichés: idealized youthfulness versus frailty and illness. To challenge these oversimplified depictions, graduate students developed a community outreach project titled ‘Seniors of Canada’. The aim of this project was twofold: (1) share images and stories of people in later life; and (2) challenge dominant narratives and stereotypes of aging. In this note, we outline the prevailing discourse of what aging ‘looks like’, how we collected stories and images, and implications for knowledge mobilization and research in Canada. This article highlights insights gained since the inception of the project, including three key learnings: (1) Building bridges across academia and community, (2) Intergenerational connection and digital tools, and (3) The power of visual storytelling. We provide a practical overview of a successful knowledge mobilization/community outreach project and showcase the power of bridging academia and community for social change.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.010 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".