Book Review: Crossroads: Comparative Immigration Regimes in a World of Demographic Change
Bibliographic record
Abstract
In Crossroads, Boucher and Gest argue that the "liberal turn" around immigration policies observed by scholars such as Freeman, Joppke, or Soysal in the late-20th century ultimately did not prevail.Instead, they find that by 2011 immigration regimes across a range of OECD and non-OECD countries converged toward a "Market Model," which heavily relies on (temporary) labor immigration and free mobility arrangements -at the expense of family and humanitarian immigration, as well as naturalization.As they write, "the Market Model permits governments to have it both ways -effectively sanitizing globalization from its purported ills but exploiting the economic benefits of human movement" (p.156) .In that sense, immigration policy seems to have aligned with liberalization tendencies visible in other areas such as labor market, trade, or social policy.Their argument, and the immigration regime typology that underpins it, rests on a thorough analysis of demographic migration data from 30 countries in Europe, the Americas, and Asia.The data collected encompass three core dimensions of immigration statistics: the "Visa Mix" (i.e., the distribution of permanent immigration visas alongside labor, family, humanitarian, or other migrant categories); the ratio of temporary labor migrants to total immigration; and the naturalization rate.The book's core chapters investigate each of these three aspects in detail, as well as their interactions.Particularly innovative is Boucher and Gest's attention to the shifting distribution of permanent immigration visas and the rise of temporary labor migration.This discussion is followed by a cluster analysis on the basis of which they advance a seven-fold typology of immigration regimes: (a) Neoliberal regimes (Australia, Canada, New Zealand, United Kingdom) are characterized by high and diverse migrant populations, high proportions of (temporary) labor migrants, and high naturalization rates.(b) Humanitarian regimes (Finland, Sweden, the United States) have high immigrant populations and naturalization rates, but admissions are International Migration Review 1-3
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".