Assimilation in multilingual cities∗ Javier Ortega City University London, CEP (LSE), CReAM, and IZA
Bibliographic record
Abstract
Using the Public Use Microdata Files of the 2001 and 2006 Canadian Censuses, we study the determinants of the assimilation of language minorities into the city majority language. We show that official minority members (i.e. francophones in English-speaking cities and anglophones in French-speaking cities) assimilate less than the "allophones " (the individuals with a mother tongue other than English or French), and that immigrants gen-erally assimilate less than natives. In addition, the language composition of cities is shown to be an important determinant of assimilation both for allophones and for official minori-ties. Finally, we show that assimilation into French in French-majority cities is lower than assimilation into English in English-majority cities even when controlling for the language composition of the cities and including a rich set of language dummmies. Nous utilisons les fichiers de microdonnées à grande diffusion des Recensements Cana-diens de 2001 et 2006 pour étudier les déterminants de la connaissance de la langue majori-taire des villes par les minorités linguistiques. Nous montrons que les minorités linguis-tiques officielles (c’est-à-dire les francophones dans les villes à majorité anglophone et les
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".