Immigration Systems in Transition: Lessons for U.S. Immigration Reform from Australia and Canada
Bibliographic record
Abstract
The history of both the Australian and Canadian immigration systems covers three distinct periods in which the countries maintained race-based models between the 1920s and 1960s-70s, implemented points-based systems after ending their race-based programs, and revised the points-based systems over time to improve their ability to select migrants and eliminate backlogs.Australia and Canada's successful implementation and revision of their immigration systems depended on governmental decisions, political and bureaucratic institutions, and data gathering operations to provide objective bases for revisions to the systems. The Australian and Canadian cases show that the United States may need to make investments in the agencies that oversee the immigration system and gather data about its outcomes. The adoption of SkillSelect and Express Entry also show that the United States may need to make dramatic revisions of the system to address backlogs and other residual components of the past system during the transition process. The effective selection of migrants and management of migration necessitates institutions that allow governments to make sometimes dramatic changes to their migration programs with public support based on actionable data. U.S. policymakers must understand these factors – and answer the questions in this report – to create an immigration system that represents the best elements of the U.S. political system and the country's immigration heritage.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".