Narratives of ethnic identity: experiences of first-generation Chinese Canadian students
Bibliographic record
Abstract
Participant observations and interviews with students, teachers, parents, and other members of the Bay Street School community were conducted over a period of three and a half years. This research is embedded in ongoing related work examining diversity and culture in the Bay Street School context. Fieldnotes written following school visits were transcribed and filed into an archival system. This work is derived from Connelly and Clandinin's work on narrative inquiry. My autobiography as a first generation Chinese Canadian provided an important framework for this study. Children of Chinese immigrant families may experience a sometimes confusing mix of influences. My research is aimed at examining their ethnic identity in a school context. I conducted a long-term narrative inquiry of the experiences of first generation Chinese Canadian students in their classroom and elementary school context to examine ethnic identity in relation to Chinese customs, English and Chinese language proficiency, physical appearance, and length of residence in Canada. I also explored the "harmonies and tensions" (Clandinin Connelly, 2002) of the inclusion of culturally sensitive curriculum events on the Bay Street School landscape. A narrative approach contributes to an understanding of the role of schooling in shaping a sense of ethnic identity and the complexities of multicultural education. Through detailed examination of selected school events, such as the Chinese Ribbon Dance and the Boyne River field trip, teacher and student experiences revealed complexities that were not initially apparent. Curriculum development and implementation involve the intersection of teacher, student, and parent beliefs, and may evolve in ways not anticipated because individual experiences shape interpretation of curriculum events in very different ways. Through these analyses, I was able to show the extent to which ethnic identity may change over time and take on different characteristics in different situations. This study suggests possibilities for a pre-service Teacher Education curriculum for diversity.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".