Caremongering and Assumptions of Need: The Spread of Compassionate Ageism During COVID-19
Bibliographic record
Abstract
The Coronavirus Disease 2019 (COVID-19) pandemic has highlighted the pervasive ageism that exists in our society. Although instances of negative or hostile ageism have been identified, critical attention to the nuances of ageism throughout the pandemic, such as the prevalence and implications of positive or compassionate ageism, has lagged in comparison. This commentary uses stereotype content theory to extend the conversation regarding COVID-19 and ageism to include compassionate ageism. We offer the "caremongering" movement, a social movement driven by social media to help individuals affected by COVID-19, as a case study example that illustrates how compassionate ageism has manifested during the pandemic. The implications of compassionate ageism that have and continue to occur during the pandemic are discussed using stereotype embodiment theory. Future actions that focus on shifting attention from the intent of ageist actions and beliefs to the outcomes for those experiencing them are needed. Further, seeking older individuals' consent when help is offered, recognizing the diversity of aging experiences, and thinking critically about ageism in its multiple and varied forms are all required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.043 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".