From China to Canada: The Identity Formation of Chinese-Canadian Adoptees
Bibliographic record
Abstract
There is a substantial amount of research done that examines the lives and experiences of Chinese-Canadian adoptees, yet not enough attention has been given to hearing from the adoptees themselves.This study engages with the personal experiences of adoptees themselves and examines how they make sense of their individual and social identities.Using an ethnographic approach, the analysis of this study is informed by semi-structured interviews from self-identified Chinese-Canadian adoptees who are 18-24 years old.The thesis expresses two findings.The first part of the analysis addresses when an adoptee self-identifies as adopted, where they believe "home" to be, when they were told they were adopted, and how adoption has been a meaning-making experience for them.Second, the analysis discusses social responses in encounters they have had with others.This includes racist remarks from others, an exploration of the family make-up of the adoptee, and where the adoptee experiences community and/or support.To understand the responses of the participants, I drew upon the methods of reflexivity and auto-ethnographic research.This study contributes information to the field of adoption work and how Chinese-Canadian adoptees make sense of their own identities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".