Understanding the Academic Journeys of Arab Immigrant and Refugee High School Students in Calgary
Bibliographic record
Abstract
The purpose of this study was to explore the academic journeys of immigrant and refugee youth of Arab descent in Calgary, Canada, to understand how this population could be better supported. Using a constructivist grounded theory design, data were collected by conducting semi-structured interviews with newly-arrived immigrant and refugee high school students of Arab descent (n = 10) and their parents (n = 3), teachers (n = 6), and administrators (n = 1) in one of Calgary’s school boards. Data were analyzed and the emerging findings point to a conceptual model with five categories that influence newcomer youth students’ learning journeys: (a) personal, (b) parental, (c) cultural, (d) scholastic, and (e) Canadian community, in addition to one overarching theme of English language acquisition. Participants recommended fostering healthy relationships between parents and children, addressing interrupted schooling in a meaningful way, and increasing English language exposure so that newly-arrived students may enjoy a smoother academic journey. Additional recommendations included developing better federal plans for newcomers, engaging community leaders in the learning journeys of newcomer youth, and nurturing open communications between the school and the parents. Suggested research includes following up with the current students, exploring other countries’ experiences with newcomer youth, and investigating how gender roles may affect the academic journeys of immigrant and refugee youth of Arab descent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".