How Different Are Immigrants? : A Cross-Country and Cross-Survey Analysis of Educational Achievement
Bibliographic record
Abstract
This paper examines differences in educational achievement between immigrants and\nnatives in ten countries with a high population of immigrant pupils: Australia, Canada, France,\nGermany, the Netherlands, New Zealand, Sweden, Switzerland, the UK and the USA. The\nfirst step of the analysis shows how far countries differ regarding immigrants? educational\ndisadvantage. In a second step, the paper compares immigrants? characteristics across\ncountries focusing predominantly on socioeconomic status, language proficiency, immigrants?\ntime spent in the host country and patterns of school segregation. Using a regression\nframework the last step of the analysis investigates how far these determinants of\neducational achievement can explain immigrants? educational disadvantage in the countries\nexamined. The paper evaluates whether results found are robust across different sources of\nachievement data: the Trends in International Maths and Science Study (TIMSS), the\nProgramme of International Student Assessment (PISA) and the Programme of International\nReading Literacy Study (PIRLS).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".