WE CAN’T AVOID IT. IT’S THERE! AGEISM EXPERIENCED BY DIVERSE CULTURAL GROUPS IN OTTAWA, CANADA
Bibliographic record
Abstract
Abstract Introduction: Discrimination based on age is pervasive across Canada. Little is known about the experiences of ageism among diverse cultural groups. The purpose of this pilot study was to explore the perceptions of ageism among culturally diverse older adults in Ottawa, Canada. Methods: Three focus groups were conducted with Chinese, Arab, and Indian older adults in Ottawa in June 2016. An 8-item protocol was developed to guide the discussions. Qualitative data were analyzed using open, axial, and selective coding. Results: Twenty-five culturally diverse older adults (9 Chinese, 6 Arab, and 10 Indian) participated in the focus groups. All described personal positive and negative examples of discrimination based on their age without being familiar with the term “ageism”. Several described their experiences with the intersection of age, race, and gender, although these interpretations varied by cultural group. Ageism in the media was also easily recognized. Participants recommended using specific content, communication channels, and organizations to counteract ageism. Discussion: This pilot study helped to illustrate that ageism is a societal problem that requires a societal solution. As Canada’s population becomes older and more diverse, important efforts are needed to raise awareness of ageism.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".