Canadian Immigration Policy for the 21st Century
Bibliographic record
Abstract
Since 9/11 there have been many changes to the external environment of immigration, a number of criticisms of current immigration policy in Canada, and several proposals for dealing with current labour market needs and settlement patterns of immigrants to Canada. In Canadian Immigration Policy for the 21st Century authors examine the issues raised by these concerns.The topics covered include international context and immigration policy goals, the role of immigration in meeting Canada's demographic and labour market needs, decentralization of immigration policy with special focus on the Quebec perspective and the recent Manitoba experience, policy responses to increasing international labour mobility, immigration data resources in Canada, the changing immigrant experience in the labour market including issues of skill recognition and the effects of business cycles on labour market integration, and social inclusion of immigrants, including the health of immigrant children and visible minority enclaves in major cities.The contributors include: Michael Abbott (Queen's University), Naomi Alboim (Queen's University), Roderic Beaujot (University of Western Ontario), David Card (University of California at Berkeley), Barry Chiswick (University of Illinois at Chicago), Gerry Clement (Manitoba Labour and Immigration Department), Don DeVoretz (Simon Fraser University), Erwin Diewert (University of British Columbia), Victoria Esses (University of Western Ontario), Alan Green (Queen's University), Gilles Grenier (University of Ottawa), and, Violet Kaspar (University of Toronto). It also includes: Ted McDonald (University of New Brunswick), Alice Nakamura (University of Alberta), Masao Nakamura (University of British Columbia), Doug Norris (Statistics Canada), Garnett Picott (Statistics Canada), Jeffrey Reitz (University of Toronto), Craig Riddell (University of British Columbia), Janice Stein (University of Toronto), Arthur Sweetman (Queen's University), Yvan Turcotte (Ministere des Relations avec les Citoyens et de l'Immigration), and Chris Worswick (Carleton University).
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.028 | 0.004 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".