Immigrant Students' Academic Performance in Australia, New Zealand, Canada and Singapore.
Bibliographic record
Abstract
Immigrants to the USA and Western Europe show a disadvantage in academic achievement that persists into the second generation. In contrast, an immigrant advantage is often seen in countries with selective immigration policies. This paper examines whether four countries with selective migration policies continue to show an academic advantage in data from PISA 2012; whether the advantage applies equally across reading, mathematics and science; and whether any advantage can be attributed to greater access to three personal (school belonging, attitude towards school learning activities and outcomes), and two teacher-related academic resources (student-teacher relationship, teacher support). Three groups (first-generation immigrant; second-generation immigrant; native-born) of 15-year-old students were compared in Australia, New Zealand, Canada, and Singapore. In Australia and Singapore, first- and secondgeneration immigrant students showed an advantage in all three subjects. In New Zealand and Canada, there was no evidence of a consistent immigrant disadvantage. The five academic resources were related to individual differences in PISA scores, but did not account for differences between migrant and native students. “Immigration is one of the defining issues of the 21st century. It is now an essential, inevitable and potentially beneficial component of the economic and social life of every country and region” (Brunson McKinley, Director General, International Organization for Migration, 2007). As a result, education systems in most countries are now responsible for ensuring that large and diverse populations of immigrant students develop the academic skills and knowledge necessary for successful resettlement. Although immigration is now a global phenomenon, it remains particularly salient in traditional “countries of immigration” (USA, Canada, Australia, New Zealand). Over 25% of Australia’s population are immigrants, and over 46% of Australians are either immigrants or have a parent who was an immigrant (Australian Bureau of Statistics, 2012). Some “countries of immigration”, including Australia, New Zealand, Canada and Singapore, are distinctive because a large proportion of their intake of long-term migrants is reserved for skilled workers (Bryant, Genc & Law 2004; Hugo, 2006;
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".