Making the most of immigration
Bibliographic record
Abstract
Canada’s immigration policy aims to promote economic development by selecting immigrants with high levels of human capital, to reunite families and to respond to foreign crises and offer protection to endangered people. Economic-class immigrants, who are selected for their skills, are by far the largest group. The immigration system has been highly successful and is well run. Outcomes are monitored and policies adjusted to ensure that the system’s objectives are met. A problematic development, both from the point of view of immigrants’ well-being and increasing productivity, is that their initial earnings in Canada relative to the native-born fell sharply in recent decades to levels that are too low to catch up with those of the comparable native-born within immigrants’ working lives. Important causes of the fall include weaker official language skills and a decline in returns to pre-immigration labour market experience. Canada has responded by modifying its immigration policy over the years to select immigrants with better earnings prospects, most recently with the introduction in 2015 of the Express Entry system. It has also developed a range of settlement programmes and initiatives to facilitate integration. This chapter looks at options for further adjusting the system to enhance the benefits it generates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.060 | 0.022 |
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".