No, I Do Belong: How Asian American and Asian Canadian Professionals Defy and Counter Workplace Racial Violence during <scp>COVID</scp>‐19
Bibliographic record
Abstract
Abstract We explore the different types of racial violence encountered by Asian American and Asian Canadians (whom we refer to as Asians) in the workplace during COVID‐19 and how they respond. Using a grounded theory approach, we found that during the COVID‐19 pandemic, Asians experienced different types of workplace racial violence, most of which manifested as microaggressions, including a revival of the yellow peril trope, physical manifestations of bordering behaviour, and identity denial. In some cases, manifestations of physical violence also emerged. The data revealed that Asians demonstrated various types of agentic responses to challenge and counter unwanted and incorrect identities conveyed by the racial microaggressions. We enhance theory by shedding light on the experiences of Asians whose voice has largely been ignored in the organizational literature. Our study draws together and contributes to the theory on racial violence and racialized identity by highlighting the different types of racial violence faced by Asians and exploring the challenges they encounter in the face of racial microaggressions. Finally, we discuss practical implications of our study results and offer insight into how organizations can help support their Asian employees.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.040 | 0.017 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".