Intersectional stereotypes of race and migration status: unfolding the societal double standards towards Asian international students in Canada / <i>Estereotipos interseccionales de raza y situación migratoria: desvelando los dobles estándares sociales hacia los estudiantes internacionales asiáticos en Canadá</i>
Bibliographic record
Abstract
Asian people are often perceived as a monolith, with the stereotypes of being competent but lacking warmth. This study examines how these racial stereotypes intersect with migration status by comparing perceived societal stereotypes towards Asian international and domestic students. Participants were 195 White students and 135 Asian students at a Canadian university. Results show that Asian students are generally perceived as more competent than warm, but Asian international students are consistently perceived as both less warm and less competent compared to their domestic counterparts. These negative stereotypes towards Asian international students are shared by both outgroup (White students) and ingroup (Asian students) members. Furthermore, among Asian students (but not White students), their endorsement of the model minority myth contributes to the societal double standards towards Asian international students. This study highlights how migration status and race jointly shape societal stereotypes, and that the model minority myth may perpetuate negative stereotypes of Asian international students. An intersectional approach incorporating immigration status offers insight into intergroup and intragroup dynamics.
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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 | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".