“It Goes under the Radar!”: In/Visibility of Anti-Asian Racism from a Canadian Youth Perspective
Bibliographic record
Abstract
Guided by Fanon’s constructs of racial epidermal schema, this study explored youth’s experiences with anti-Asian racism (AAR) and their perspective on how AAR is formed and maintained. We recruited 36 Asian youth (age range 14–23 years) residing in a metropolitan city in Canada to attend a semi-structured focus group. Results from the reflexive thematic analysis indicated the tenuous lines of invisibility and visibility of AAR—in a Whiteness dominant racial schema. When Asian youth are objectified within the boundary of Whiteness norms, AAR is pervasive but inconspicuous, happening early and frequently in their life but unrecognized. When they are objectified as threats to Whiteness dominance such as during the COVID-19 pandemic, AAR is visible in forms of overt discrimination, violence, and hate against Asians. Youth attributed roots of AAR to White supremacy and critically discussed their racialization process. Their reflexive insights serve as a form of resistance to AAR.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".