Upholding dignity during a pandemic via Twitter
Bibliographic record
Abstract
Background: This article investigates how people invoked the concept of dignity on Twitter during the first year of the COVID-19 pandemic, with a secondary focus on mentions of dignity in the context of older adults and ageing. Methods: We report the results of a study that combines text analytic and interpretive methods to analyze word clusters and dignity-based themes in a cross-national sample of 1,946 original messages posted in 2020. Results: The study finds that dignity discourse on Twitter advances five major themes: (a) recognize dignity as a fundamental right, (b) uphold the dignity of essential workers, (c) preserve the dignity of at-risk populations, (d) prevent cascading disasters that exacerbate dignity's decline, and (e) attend to death, dignity, and the sanctity of life. Conclusions: Moreover, messages focusing on older adults lamented the disproportionate death toll, the terrible circumstances in long-term care homes, the added impact of suspended meal delivery services and the status of older people living below the poverty line.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".