Becoming Multicultural: Immigrants and the Politics of Membership in Canada and Germany
Bibliographic record
Abstract
Canada and Germany are often considered polar contrasts when it comes to immigration and multicultural-ism. Yet in this provocative, insightful, and original analysis of the two nations, Triadafilopoulos observes that the two countries have more in common than may initially be apparent. He notes that that Canada and Germany followed similar trajectories over the 20th century. “Both countries began the century by prohibiting the entry and incorporation of immigrants deemed undesirable because of their putative racial or ethno-national char-acteristics[…] Yet by the end of the century, both […] had developed into de facto multicultural societies ” (p. 2). This outcome came about as both nations responded to labour force needs and adjusted to the emergence of new international ethical norms against racism. Within this common trajectory, the research reported in this book documents wide differences in policy process and specific immigration policy and multicultural outcomes between the two nations. Becoming Multicultural addresses the politics of immigration for the two national cases by carrying out a long historical analysis of these politics, covering three periods: the first wave of globalization in the late 19th and early 20th centuries, the dismantling of colonialism in the mid-20th century, and the resurgence of immigra-tion and foreign worker flows in the late 20th century. This breadth of perspective for the comparative study of immigration and multiculturalism builds on the work initiated by various eminent scholars, such as Aristide
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.041 | 0.015 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".