The Experience of Anti-Chinese Racism in the Greater Toronto Area (GTA) Before and During Covid-19: An Intersectional Analysis
Bibliographic record
Abstract
Abstract: This paper aims to fill a lacuna in existing literature by adopting a feminist and intersectional framework and analysis to examine the experience of racism of individual Chinese residing in the Greater Toronto Area (GTA), and to explicate how macro and meso-structural processes impact Chinese individuals and communities during COVID-19. We draw on data analysis from a Social Sciences and Humanities Research Council of Canada (SSHRC) funded research project, and focus our analysis of the experience of anti-Chinese racism before and during COVID on i) context of racialization and racism, i.e., geopolitics, Chinese culture and identity, and media representation; and ii) experiences of racialization and racism, in the form of stereotyping, microaggression, and verbal acts of racism. We demonstrate that anti-Chinese racism has persisted prior to COVID-19, however, the pandemic exacerbates precipitating racist ideologies, policies and practices, allowing them to manifest and proliferate. In particular, our paper elucidates how different forms of anti-Chinese racism interact with individuals' intersectionalities (i.e., race, class, gender, age, ability, English/French fluency, immigration/citizenship status, etc.) to further complicate how individuals are differentially targeted and how they experience racism differently. As well, our paper illuminates how individual interviewees utilize their agency to devise strategies to deal with anti-Asian racism. Résumé: Cet article vise à combler une lacune dans la littérature existante en adoptant un cadre et une analyse féministes et intersectionnels pour examiner l'expérience du racisme des chinois résidant dans la région du Grand Toronto ( RGT ), et pour expliquer comment les processus macro et mésostructurels ont eu un impact sur les individus et les communautés chinoises pendant la COVID 19. Nous nous appuyons sur l'analyse des données d'un projet de recherche financé par le Conseil de recherches en sciences humaines du Canada (CRSH) et nous centrons notre analyse de l'expérience du racisme anti-chinois avant et pendant la COVID sur i) le contexte de la racialisation et du racisme, c'est-à-dire la géopolitique, la culture et l'identité chinoises, et la représentation médiatique ; et ii) les expériences de la racialisation et du racisme, sous la forme de stéréotypes, de micro-agressions et d'actes verbaux de racisme. Nous démontrons que le racisme anti-chinois a subsisté avant la COVID 19, mais que la pandémie exacerbe les idéologies, les politiques et les pratiques racistes qui l'ont précédé, leur permettant de se manifester et de proliférer. Plus particulièrement, notre article élucide la manière dont les différentes formes de racisme anti-chinois interagissent avec l'intersectionnalité des individus (c'est-à-dire la race, la classe, le sexe, l'âge, les capacités, la maîtrise de l'anglais ou du français, le statut d'immigrant ou de citoyen, etc. En outre, notre article met en lumière la manière dont les personnes interrogées utilisent leur pouvoir d'action pour élaborer des stratégies de lutte contre le racisme anti-asiatique.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".