Language Minority Students’ Status: One Large Scale Exam and Two Countries
Bibliographic record
Abstract
Educational policies are dynamic and can be revised when needed. In order to analyze how successful a country is, what their rank is among others and what a country needs to improve, international exams, like PISA, are conducted by authorities. Also, there are many countries with a large immigrant population. In this regard, the educational policies should include some regulations for immigrant students’ education. Canada and Belgium have been chosen as participant countries of this study since they are bi/multilingual countries. Even though they are similar to each other with their immigrant population, the education policies in these countries differ. This study aims to compare the success of the immigrant students in reading skills in both countries as well as their sense of belonging and their parents’ education by utilizing PISA-2015 data. The results display that the immigrant students in Canada have outperformed their peers in Belgium. Furthermore, the immigrant parents in Canada are more educated than those in Belgium, and Canadian immigrant students show lower sense of belonging to school when compared to the peers in Belgium. Although these factors are controlled, the Canadian immigrant students outperform; therefore, some remarks for education policies in bi/multilingual countries can be made.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".