Bibliographic record
Abstract
Abstract This autoethnographic narrative shows how discourses of belonging for racialized identities within Canada’s mosaic are bounded by history, cultural politics, and attendant social struggles. Using an intersectional framework of Asian Critical theory, politics of location, and cultural capital, this paper demonstrates how ideologies of belonging are sustained by processes of cultural and institutional socialization which maintain hierarchies privileging some social groups over others and produce racial/ized difference and inequities within Canadian citizenry. As a second-generation of Chinese ancestry born and raised in Vancouver, Canada, my lived experiences in a predominantly white English-speaking environment illustrate how my status as “model minority” or “honorary white” has been a precarious position. Bonilla-Silva warns us that “honorary white” positioning may be revoked in times of economic, racial or ethnic tension. Dramatic increases in anti-Asian hate incidents during the 2020 Coronavirus pandemic—earning Vancouver, BC, the title of the “anti-Asian hate capital of North America”—is an example of how these racialized statuses are paradoxical designations which deny the existence of social inequities. Critical research must interrogate how the continued use of mis-aggregated data that essentializes diverse population groups and perpetuates harmful distortions of Canadian citizenry contribute to, rather than dismantle, discourses of race in “multicultural” Canada.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.027 | 0.041 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".