Factors Affecting Academic Outcomes among Immigrant and Refugee Youth Arriving to Calgary from Arab Countries
Bibliographic record
Abstract
Canada has a long history of welcoming immigrants and refugees from all over the world. Currently, newcomer youth represent 25 % of all Canadians under the age of 18 (Statistic Canada, 2012). Furthermore, with the recent influx of Syrian refugees, this percentage is expected to increase, as within the 25,000 refugees who arrived in Canada until February 2016, 48.8 % were under the age of 18 (IRCC, 2015). The Canadian school system has had little time to react to this influx. Research among immigrant and refugee youth focused mainly on the three main Canadian cities of Montreal, Toronto and Vancouver, (Anisef & Kilbride, 2003) and very little is known about newcomer youth in Calgary, Alberta. Prior studies have shown that newcomer youth have faced challenges, (Ochocka, et al., Kelly, 2014) however, very little is known about what kind of challenges might immigrant and refugee youth from Arab countries have and, how would they face it as compared to other ethno-cultural groups of newcomers. The purpose of the study was to explore factors helping or challenging youth in terms of academic outcomes in Calgary, Alberta, and how this population of students can be better supported.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".