Work, Welfare, and Wanderlust: Immigration and Integration in Europe and North America
Bibliographic record
Abstract
Europe and North America have long diverged in their immigration policy. Simply put, Europe was from the early 1800s until the 1950s a continent of emigration, whereas the United States and to a lesser degree Canada were quintessential countries of immigration. Canada and the United States encouraged Northern European immigration with the goal of building white, Anglo-Saxon settler societies; Europe encouraged emigration with the goal of exporting surplus population and unemployment (Germany, Italy) and/or empire building (the United Kingdom). In the postwar years, divergence continued. The United States and Canada abandoned the race-based, exclusionary inflection of their immigration policies, and opened their doors to an extraordinary migration from East and South Asia, the West Indies, Latin America, and Africa. European nation-states tried to have their cake and eat it too: They tried to harness the economic benefits of mass unskilled labor while ensuring that the migration was temporary. These efforts largely failed: The liberal constitutional order that is common to Europe and North America meant that the immigrants were not simply workers but rights-bearers, and European courts frustrated national efforts to guarantee the migrants' return. The result, by the 1990s, was a demographic makeup that looked broadly similar on both sides of the Atlantic. European and North American societies were multi-ethnic; the bulk of migrants and ethnic minorities lived in their cities; and (with Canada partially excepted) the migration patterns were dominated by family reunification.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".