More than a New Country: Effects of Immigration, Home Language, and School Mobility on Elementary Students' Academic Development
Bibliographic record
Abstract
Few studies have quantified the effects on academic performance; none has investigated, as this study does, the effects of immigration, home language, and school mobility on academic development over time. What makes this study unique is its melding of sociological and psychometric perspectives – an approach that is still quite new. Logistic regression was used to analyze data from Ontario’s 2007-2008 Junior (Grade 6) Assessment of Reading, Writing and Mathematics, with linked assessment results from three years earlier, to investigate students’ academic achievement. The focus of this study is on whether the students maintained proficiency between Grades 3 and 6 or achieved proficiency in Grade 6 if they were not proficient in Grade 3. The results indicate that Grade 3 proficiency is the strongest predictor of Grade 6 proficiency and that home language or interactions with home language are also significant in most cases. In addition, students who speak a language other than or in addition to English at home are, in general, a little more likely to be proficient at Grade 6. Most students who were born outside of Canada were significantly more likely than students born in Canada to stay or become proficient in Reading, Writing, and Mathematics by Grade 6. These results highlight the importance of considering the enormous heterogeneity of immigrants’ experiences when studying the effects of immigration on academic performance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".