2005), Immigration as a labour market strategy: European and North American perspectives, Migration Policy Group
Bibliographic record
Abstract
Canadian immigration policy, the economic outcomes of immigrants, and the economic impact of immigration on the Canadian economy have all changed, frequently quite dramatically, in recent decades. Immigrants now make up about 18 per cent of the resident population, and this percentage is increasing given a relatively low fertility rate and an annual immigration rate of about 0.7 per cent of the population. Following policy changes in 1962 and 1967, the distribution of source countries has shifted dramatically as have many of the characteristics of new immigrants. Whereas, for example, prior to 1960 about 90 per cent of immigrants came from Europe and only 3 per cent came from Asia, in the 1990s only 20 per cent came from Europe and 60 per cent arrived from Asia. Associated with the change in source region, there has been a decrease in English and French language knowledge, and an increase in the frequency of immigrants being members of a visible minority. Unfortunately, recent decades have also seen a very substantial decline in immigrant labour market outcomes and an increase in poverty (formally low income). In 2000, the poverty rate for those who arrived anytime in the 1990s was 35 per cent. Male (female)
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".