Constructing and challenging mixed-race identities among South Asian women in Canada
Bibliographic record
Abstract
This thesis speaks to the experience of being a mixed-race woman and of South Asian descent in Canada. It is a compilation of stories (not just my own), but of other women who are half South Asian and struggling to create an identity in a Canadian context. All of the women have one parent who is Pakistani or East Indian, and all of them are living in Canada and are between the ages of 20–30. The women discuss the specific nature of their experience being mixed-race in a society that is still very hostile towards the idea of miscegenation. Being half South Asian makes this experience even more difficult for some of them, since South Asian identity is very complex and situationally specific. The impact that this has on their lives is discussed at length, as well as ideas that can enable schools to help children who are suffering from similar problems. Despite differences in experience, all of the women agree that schools need to be improved in dealing with such issues as passing, internalised racism, racial violence within families and communities, as well as racial violence in schools. By discussing these issues, we can begin to create schools which are not only more helpful for mixed-race children, but for all children in Canada.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.085 | 0.023 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| 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".