Assessing Immigrant Integration
Bibliographic record
Abstract
Assessing Immigrant IntegrationUltimately, the successful integration of immigrants depends on the relationships that develop among the diverse population groups that their arrival creates.Today, this is mainly a question about "visible minorities," the racialized populations of non-European backgrounds.The historical record shows that immigrants to Canada from European backgrounds have been to a considerable degree successfully integrated in Canadian society, including economic, social, political, and cultural domains.Earlier negative distinctions among persons of German, Italian, Polish, and other European backgrounds have faded to insignificance, while cultural traditions continue to be celebrated.In today's world, the distinctions that matter are those between these groups, now often referred to collectively as "whites" -both colloquially and in official government statistics -and the various immigrant groups of non-European origins considered as racialized: Chinese, South Asian, and Black peoples being the largest among a host of others.This leads us to a key question: Are immigrants within the definition of racialized populations in Canada on course towards successful integration?It is useful to ask this as part of policy development on immigrant integration to help understand the scope of the problem.Various answers are given.They reveal both problems of integration and promising signs of progress towards greater integration.There is evidence of widespread discrimination of new immigrants and racialized minorities in the country.At least since the 1980s, academic research has demonstrated this, and the fact has been officially recognized by the federal government.Disadvantages have persisted over time, and vary among racialized groups, according to 2021 census data.89 Rates of povertydefined as disposable family income below basic living costs in one's local community -were higher in 2020 across most racialized minorities compared to the white population, and they remained 89 Christoph Schimmele, Feng Hou, and Max Stick, "Poverty among Racialized Groups across Generations," Economic and Social Reports 3, no.8 (2023),
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".