A Plague of Racism: An Analysis of the Racialization of the Plague Throughout History
Bibliographic record
Abstract
The Corona virus is not unique in its racialization of disease. Throughout history pandemics have been blamed on particular nations and given names based on that—the Spanish flu, or the “Russian Influenza”. This is a two-prong issue of racism in pandemics, firstly is blaming the issue on a particular group, and second is not providing proper health care to racialized groups. In Canada today, Aboriginal, Metis and Inuit people provide inadequate health care based on their remoteness, in America today black communities are disproportionately affected by the Carona virus. And today with the Carona virus there has been a massive increase in anti-Asian hate crimes. This is not unique in history however, the plague that devastated much of Europe and later India was blamed primarily on racialized groups. These groups became seen as simultaneously the victims and the perpetrators of the disease. The plague represents perfectly the combination of improper treatment of disease based on race and the blaming of a pandemic on a racialized group. The plague alone has been blamed on Chinese people in Hawaii, Indians in India and Jews in Europe. Although the racialization of disease is not new, it is based on incorrect assumptions and is incredibly problematic. In particular, India is one of the best examples of the ignorance involved in the racialization of disease. In the case of India, the British government ignored the fact that the poor living conditions were caused primarily by their own actions and not those of the “dirty natives”, ignored the fact that the disease did not originate in India, ignored that Britain itself experienced a more severe pandemic of the same bacterium and ignored traditional methods of healing. Department: Interdisciplinary Dialogue Project Faculty Mentor: Dr. Aidan Forth
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".