The hidden power of provincial and territorial immigration programs in shaping Canada’s immigration landscape
Bibliographic record
Abstract
Abstract The Canadian immigration system is unique in that subnational governments play a significant role in selecting immigrants through Provincial Nominee Programs (PNPs), which empower nine provinces and two territories to actively select (“nominate”) economic immigrants. Collectively, PNPs have become the country’s largest economic immigration program, but they are also the least studied, leading to a lack of understanding, transparency, and accountability. Using a subnational comparative method, this study examines 78 active subnational immigration programs (policy outputs), investigating policy design, requirements, and distribution of nominations in 2021–2022. We assess whether PNPs contribute to broader changes in the Canadian immigration regime. First, our analysis reveals the prevalence of employment-based streams and prearranged work as a selection criterion. Second, we show nuanced policy outputs in the progression toward a two-step system, with provincial variation in requirements for prior Canadian experience. Third, while PNPs are open to low-skilled workers, programs tailored exclusively to this group remain relatively limited. This comparative analysis reveals significant inter-provincial variation, and highlights the importance of a “disaggregated” evaluation of the migration state at the subnational level.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".