What Is in a Name: The Covid-19 Virus Naming Variants and Their Impact on Chinese Canadian Community
Bibliographic record
Abstract
Chinese Canadians have settled in Canada since as early as in 1788 (Lai, Leong “Chinatown Series – Toronto”). The first large scale of Chinese immigrants came to Canada during the Fraser River Gold Rush in the 1850s, followed by waves of Chinese railroad workers in the end of the 19th century. Most Chinese Canadians have changed their sojourner mentality to calling Canada home from the 1960s, the latest documented by researchers (Leong; Poy). While most Chinese Canadians see themselves as Canadians, but are they seen as ones? The COVID-19 Pandemic has once again taken this notion into spotlight. From anti-Asian racism, naming of the virus, and contesting Chinese Canadians as simply Chinese, the pandemic highlights the subtle, unconscious, and systematic discrimination against Chinese Canadians. In this presentation, I’ll discuss various names used to refer to the COVID-19 virus, specifically the ones related to race and origins, and reveal the anti-Chinese racism associated with these terms. I argue these terminologies, most often claim to have derived out of convenience or common sense, denote deep rooted racist classification and information practices.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.024 | 0.012 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".