Anti-Chinese stigma in the Greater Toronto Area during COVID-19: Aiming the spotlight towards community capacity
Bibliographic record
Abstract
Due to the geographic origins of the first major outbreak of COVID-19 in Wuhan, China, individuals of Chinese ethnic origin around the world have experienced discrimination, xenophobia, and racism during the pandemic. Discriminatory actions have ranged from outright physical aggression to subtle microaggressions. While reports (both media and academic) have highlighted such incidents, this paper argues that when the conversation starts and stops at the reporting of experiences of stigma, the narrative remains as the victimization of the community. Instead, instances of COVID-19 stigma and discrimination are only one aspect of this story, where other aspects include a deeper understanding of the community itself along with an awareness of the capacity that the Chinese diaspora community brings forward to help overcome COVID-19. We focus our discussion on the Greater Toronto Area (GTA) in Canada, a global urban center that has a sizeable ethnic Chinese diaspora community, and argue that highlighting the early actions that the community took to help broader society in dealing with COVID-19 at the start of the pandemic may help to reframe anti-Chinese stigma during the pandemic. These early actions include physical distancing, mask-wearing, sanitation and advocacy. Findings for this case-study are informed by media monitoring and interviews with 83 individuals identifying as ethnically Chinese living across the GTA.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".