Analysis of Bias in International Education from the Perspective of Intersectionality Theory
Bibliographic record
Abstract
International students encounter more psychological, structural, and social challenges that negatively affect their mental health, academic engagement, and college readiness. Collectively, these challenges have a crucially negative effect on their academic achievement. To be specific, previous researchers often attribute international students' lower academic performance to experiences of sexism or racism alone. However, intersectionality theory suggests that harmful stereotypes can compound and lead to heightened discrimination; for instance, black female students experience significantly more discrimination than white male students, thereby considerably limiting their opportunities to achieve excellent academic success and engage in university social activities. Thus, by reviewing previous findings, the current study argues that the multiple disadvantaged identities of international students compound to negatively affect their academic performance, particularly when they have intersections of both underprivileged gender and race. The current study highlights the critical role of intersectional identities in international students' differential vulnerability, making them more vulnerable to intersectional biases than native students. Furthermore, the up-to-date intervention proposed to promote education equity for international students is discussed. Fortunately, numerous classroom designs attempt to engage international students in classroom education and are deployed to satisfy international students' diverse needs, the most sophisticated of which is the flipped classroom.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".