AGECovP: identifying ageism and analyzing COVID-19 discourse on older adults in YouTube
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The COVID-19 pandemic significantly impacted older adults, generating widespread online discussions that revealed how this at-risk population was perceived. Understanding these portrayals is essential, as public discourse influences societal perceptions of aging and impacts policies and practices affecting older adults. Past research highlights that ageist stereotypes and attitudes frequently surface in public discussions, shaping the experiences of older individuals. The current study presents AGECovP, a comprehensive dataset featuring a diverse collection of YouTube videos, a leading social media platform. AGECovP is designed to provide researchers with meaningful insights into how older adults were portrayed during the pandemic and how topics such as conspiracy theories, misinformation, and the anti-vaccine movement were framed in relation to aging populations. In addition, the dataset includes a set of labeled comments indicating the presence of ageist content, enabling researchers to perform ageist detection and analyze ageism in online discourse. By providing a resource for examining both overt and subtle forms of ageism, AGECovP contributes to the development of tools and methodologies for addressing bias against older adults. This dataset fosters actionable insights into societal attitudes, enhancing the development of inclusive policies and interventions. Our data is available at: https://zenodo.org/records/15800324.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it