Ageism and COVID-19: what does our society’s response say about us?
Bibliographic record
Abstract
The goal of this commentary is to highlight the ageism that has emerged during the COVID-19 pandemic. Over 20 international researchers in the field of ageing have contributed to this document. This commentary discusses how older people are misrepresented and undervalued in the current public discourse surrounding the pandemic. It points to issues in documenting the deaths of older adults, the lack of preparation for such a crisis in long-term care homes, how some 'protective' policies can be considered patronising and how the initial perception of the public was that the virus was really an older adult problem. This commentary also calls attention to important intergenerational solidarity that has occurred during this crisis to ensure support and social-inclusion of older adults, even at a distance. Our hope is that with this commentary we can contribute to the discourse on older adults during this pandemic and diminish the ageist attitudes that have circulated.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".