Interregional Mobility of Latin American Immigrants in Canada: Explorations Using Tax Filer Data
Bibliographic record
Abstract
Using data from the Longitudinal Immigration Database (IMDB), which links tax filer information to provinces of landing information and current regions, the author carried out data explorations regarding the interregional mobility of 98,440 Latin American immigrants arriving in Canada between 2000 and 2014. These were observed in the tax year 2014. The interregional mobility of citizens from 15 citizenship countries was examined: Argentinians, Bolivians, Brazilians, Chileans, Colombians, Cubans, Ecuadorians, Salvadoreans, Guatemalans, Hondurans, Mexicans, Nicaraguans, Peruvians, Uruguayans, and Venezuelans. Immigrants were allowed entry into Canada under various immigrant intake classes such as economic, family, and refugee. Examination of retention and net migration rates showed that Alberta and British Columbia were among those who benefited the most from Latino immigrant inflows during the observation period. About one in five Latinos had moved outside their original landing region by the tax year 2014. Citizens of various nationalities left the Atlantic, Quebec and Manitoba regions for other ones. Interregional mobility was found the highest among males, earlier arrival cohort members, those with higher educational levels and economic principal applicants. Colombian citizens were the most mobile group while Nicaraguans, Bolivians, and Ecuadorians were the least mobile. The regional triangle constituted by Alberta, Ontario, and Quebec was found to be the dominant one in the network of all migratory exchanges. Tracking the interregional mobility of Latin American immigrants to Canada after arrival provides interesting insights into how this particular immigrant population is redistributed, how it may respond to the needs of regional economies, and also speaks to the success of immigrant integration and resettlement of Latin American immigrants in particular regions of Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.020 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".