Bibliographic record
Abstract
We consider a bilingual country with immigration, where only agents who share the same language can produce together. Immigrants cannot communicate with each other nor with natives unless they learn to speak one of the two languages of the country. We model immigrants’choice of language. With an exogenous language composition of natives, immigrants tend to choose the language of the majority. Endogenising the choice of minority natives to become bilingual does not alter the set of production partners of other minority members but reinforces the incentives of the immigrants to learn the majority language. In order to learn the minority language, immigrants would require a larger subsidy the smaller the minority. Using Canadian language data from the 2001 Census at the city level, we show that the assimilation of anglophone minorities into French negatively and strongly depends on the size of the city’s anglophone minority, while the assimilation of francophone minorities into English is lower only when the number of Francophones is above a 5% threshold. In addition, the size of the anglophone group is the driving force in explaining immigrant assimilation in both types of cities: a larger anglophone minority deters immigrant assimilation into French in francophone cities, and a larger anglophone majority fosters immigrant assimilation into English in anglophone cities. Instead, the size of francophone minorities (respectively, majorities) does not reduce (respectively, increase) assimilation into English (respectively, French). Finally, overall assimilation displays the same pattern as immigrant assimilation. JEL classi…cation: F22, J 15
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".