Investigating Migration through the Phenomenon of School Integration: Anaya’s Experience of Resettlement in Canada
Bibliographic record
Abstract
Using a social justice framework, this arts-based engagement ethnography (ABEE) investigated the phenomenon of school integration among newcomer youth who migrated to Canada. Defined broadly, this phenomenon captures the adjustment of newcomer youth across all aspects of student life – both inside and outside the school context, including English Language Leaning (ELL), academic performance, classroom behaviour, social networking, emotional and familial well-being, involvement in school life, and understanding of the educational system. Specifically, two research questions were investigated: 1) How do newcomer youth experience school? and 2) How do these experiences influence their positive integration into the school system? Results from one participant – Anaya, a 19-year-old cisgender female who migrated to Canada from India with her family at the age of 12 – are presented to illustrate the manner in which the phenomenon of school integration can be used as a point of entry to study migration. These result included the following five themes: 1) The Struggle to Fit In / “I regard myself as a social outsider”, 2) Managing Parental Expectations / “Our values started to clash”, 3) Implications of Self-Exploration / “I was kind of in the middle”; 4) Finding a Passion and Getting Involved / “I became a lot more friendly”, and 4) Embracing a Multicultural Identity / “I am reembracing my heritage.”
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.044 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".