Retweeting Covid-19 disability issues: Risks, support and outrage
Bibliographic record
Abstract
The Covid-19 pandemic has greatly uneven impacts on sectors of society. People with disabilities are particularly vulnerable to it and so it is important to understand both the disability perspective and the role of social media. This information may help to reduce the risk from the disease. In response, this article uses thematic analysis to investigate 59 disability-related tweets from March 10 to April 4, 2020 that were retweeted at least 500 times, with a quarter of a million retweets altogether. This approach generates quick insights into widely resonating disability-related issues. The results suggest the value of Twitter for disseminating information about the risk, offers or requests for support, the ability of many people with disabilities to adjust to the changes well, and information about individuals with the disease. In addition, there was outrage at suggestions that the disease was less serious because young people without disabilities were relatively low risk, and that people with disabilities might be denied equal access to medical treatment. As one tweet pointed out, people in less vulnerable categories should not be told on Twitter or elsewhere that the disease is less relevant to them because their actions can impact others through social spreading.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".