Bibliographic record
Abstract
Q: Why don’t chefs find coronavirus jokes funny? A: They’re in bad taste. Jokes and political satire have not been on lockdown during the COVID-19 pandemic. Books, blogs, memes and one-liners abound, all poking fun at the terrifying and deadly matter of the viral scourge that has killed millions of people around the globe. Disabled and older people, especially those who are poor and/or racialised, have been hardest hit by the pandemic, and have also been made the targets of Western racist humour, as well as anti-racist satire. My particular concern is with the paradox of politically woke satire carrying out its critique through the denigration of disability. Such political satire exposes racist and bogus beliefs circulating in the pandemic, but it does so by using terms that reference, sometimes with vehement hatred, disabled people. This is more than blaming the victim; it fails to acknowledge how social responses to the pandemic have had a devastating effect on disabled people, who account for six out of ten COVID-19 deaths (Aljazeera, 2021). Through an analysis of one example of this common satirical trope, I aim to show that a critical disability studies perspective can awaken political imagination by not reproducing traditional hierarchies of humanness. Consider this: Outbreak TORONTO – REPORT: OUTBREAK OF IDIOCY SPREADING 10000 TIMES FASTER THAN CORONAVIRUS Public health officials in Toronto have confirmed its first 50,000 cases of being a misinformed fuckwit as xenophobic conspiracy theories and tales of false cures continue to spread across social media. “Becoming a complete moron during an infectious disease outbreak is far more viral than we first thought,” said Dr Jeanne Smith of Toronto Public Health. “Fact resistance is abnormally high especially among the dullard population, and the bottom 5% of your graduating high school class.” Tens of thousands of people were affected by a novel fake news claim that the Chinese government was developing coronavirus at Canada’s National Microbiology Lab leaving at least 10,000 people stupider. Patients are usually asymptomatic until they open their mouths or start tweeting. Aunts spreading rumours about 100% natural cures for the virus on Facebook have been quarantined while racist uncles at dinner tables were ball-gagged as a precaution.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.061 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".