From Academic to Personal: Addressing Asianness in Ontario Education
Bibliographic record
Abstract
This study explored the complexities of Asian Canadian experiences in educational spaces. In particular, I considered how various stereotypes and discourses of Asianness contribute to an environment in which Asian students are rendered both privileged and marginalized, seen and unseen, supported and excluded. Current research on Asian Canadians reveals a limited understanding of the intricacies of Asian experiences in educational settings. Moreover, attention to the particular needs of this group is often not considered in policies designed to address racial and ethnic equity in schools. Using an autobiographical approach, I explored and analyzed four personal vignettes to gain perspective into my experience of racial stereotypes, prejudices, and discrimination. I framed this project using Asian Critical Theory to discuss how my identity developed as a female Chinese Canadian student and teacher in Ontario. I used these stories, alongside theory and literature, to interrogate the relevance, applicability, and utility of equity and inclusive practices in teaching. Throughout, I ask how the racialized discourses and stereotypes around Asianness inform my identity as an Asian Canadian.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.047 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".