Ageism in Manitoba workshop summary
Bibliographic record
Abstract
As part of the Centre on Aging’s outreach to bring awareness of issues faced by older people in the Province, the Centre hosted a half day workshop on January 23, 2019 that focused on ageism in Manitoba. Over 70 participants took part in this event where ageist stereotypes in society were examined, and participants were asked to reflect on their own internal ageist ideas. A short presentation given by Centre Director Dr. Michelle Porter, outlined the existing definitions in the academic literature and from international organizations (e.g., World Health Organization) on ageism, discussed the effects, and looked at ways to reduce ageism in society. Several group and individual activities required participants to think of and identify examples of ageism; what actions can be taken; and discuss what specific types of strategies or activities/programs could be studied in Manitoba. This document provides a brief synopsis of the individual and group activity reporting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.006 |
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".