Immigrants and ‘New Poverty': The Case of Canada
Bibliographic record
Abstract
Studies of the economic status of recent immigrants to the United States have questioned the generalizability of some earlier findings based on assimilation theory. In Canada, however, little research has been done on this issue, and that has left mixed results. The present study attempts to address the economic performance of immigrants in Canada through an examination of their poverty status. This is particularly important now because, since the late 1980s, many industrial nations including Canada have been subjected to an unexpected surge of poverty known as ‘new poverty.' The findings indicate that immigrants in Canada are consistently overrepresented among the poor; that their poverty rates are particularly high in larger cities, which have larger concentrations of immigrants; and that among immigrants, the poverty rates are higher for visible minorities, who are mostly recent immigrants. One particularly surprising finding was that the second-generation immigrants, who were expected to outperform their parents, had higher poverty rates. A series of logistic regression models are developed to shed some light on the possible reasons behind these trends. Of the three sets of potential contributors – human capital, assimilation and structural factors – the first two were found more relevant. The models also revealed that the human capital factors were less rewarding for immigrants than natives.
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".