Impacts of the Point System and Immigration Policy Levers on Skill Characteristics of Canadian Immigrants
Bibliographic record
Abstract
This paper examines how changes in immigration policy levers actually affect the skill characteristics of immigrant arrivals using a unique Canadian immigrant landings database. We first review the Canadian experience with a point system as part of its immigrant policy. Section III of the paper describes some overall patterns of immigrant arrivals since 1980. Section IV identifies some relevant hypotheses on the possible effects on immigrant skill characteristics of the total immigration rate, the point system weights and immigrant class weights. The “skill” admissions examined are level of education, age, and fluency in either English or French. Regressions are then used to test the hypotheses from Canadian landings data. It is found that (i) the larger the inflow rate of immigrants the lower the average skill level of the arrivals; (ii) increasing the proportion of skill-evaluated immigrants raises average skill levels; (iii) increasing point system weights on a specific skill dimension indeed has the intended effect of raising average skill levels in this dimension among arriving principal applicants; and (iv) increasing the proportion of skill-evaluated immigrants appears to have the strongest effects among the immigration policy levers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".