Bibliographic record
Abstract
This paper examines the racialization processes of skilled Korean women who immigrated to Canada after the Asian financial crisis and had been residing in Canada less than five years at the time of interview. In-depth interviews with twenty-five Korean women living in the Greater Toronto area were employed for this qualitative study. The theoretical frameworks used to analyze and interpret the interview data are anti-racism and postcolonial feminism. Focusing on these twenty-five Korean immigrant women’s labour market integration in Canada, this paper investigates how the settlement experiences of skilled Korean women are shaped in the context of a gendered and racialized Canadian labour market. Covert forms of racism are discussed, including accent discrimination, unrecognized foreign credentials, and the unreasonable requirement of Canadian work experience. The paper also looks at how stereotypes emphasizing shyness, passivity, and backwardness attached to Asian women are often experienced by Korean women in their workplace. At the same time, the Korean women in this study are not passive victims, but struggle with their racialized identity formation, either by attempting to assimilate into perceived mainstream norms or by resisting against the unreasonable treatment to which they are exposed. The findings of this study challenge the discourse of multicultural Canada, in which Canada is described as uniquely characterized by liberalism, tolerance and gender equity; indeed, within Canadian multiculturalism, culturalization has become another technique of racialized oppression.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".