Cultural Insensitivity within Canadian Post-Secondary Institutions: An Analysis of North Indian International Students Experiences
Bibliographic record
Abstract
"Cultural Insensitivity within Canadian Post-Secondary Institutions: An Analysis of North Indian International Students Experiences -Gagneet Kaur (104873407), B.A (Honours) Criminology & Sociology & Dr Clayton Smith; Faculty of Education, University of Windsor In the recent times, Canada has seen an exponential rise in the number of international students enrolling in post-secondary institutions. As of 2018, over 300,000 foreign students were issued Canadian study permits, and more than half of them belonged to the Indian Subcontinent (Immigration, Refugees and Citizenship Canada, 2018). While this number continues to rise, concerns over racial insensitivity against foreign students continues to be a cause of concern. A majority of Canadian universities are not representative of Canada's growing multicultural population and adhere to a set of Eurocentric policies (Samuel & Burney, 2003). Despite a policy based adherence to equity, universities are not equipped to manage the needs of culturally diverse students, simply because they lack ‘cultural toolkit' to deal with diversity issues (Henry & Tator, 2009). A majority of Indians in Canada, belong to the North Indian state of Punjab, which has become the epicenter of Canadian immigration in India. Additionally, some North Indians tend to stand out because of their choice to wear a turban, thus increasing their likelihood of being racially profiled. Since university marks a crucial developmental phase in the life of a student, experiencing incidents of racial bias can have far reaching impacts on the mental well-being of victimised students. Through the purpose of my study, I want to conduct focus group discussions and interviews to understand the nuanced experiences of racial bias faced by North Indian international students on campus, and consequently highlight the need for cultural awareness initiatives for university personnel, that will help in making Canadian universities collaborative and inclusive spaces.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.006 |
| 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".