Immigration, Policy Learning, and State-Society Relations: Guestworker Recruitment in Switzerland and Germany
Bibliographic record
Abstract
Advanced democracies, it is commonly argued, are unable to prevent the permanent settlement of migrant workers because of the normative, legal, and economic constraints of liberalism. This paper seeks to qualify these claims by examining the politics of recruitment and settlement in two of Europe’s archetypical guestworker countries, West Germany and Switzerland. While both countries responded to comparable labor market pressures with the establishment of migrant recruitment systems, guest worker settlement in Switzerland proceeded at a much slower pace than in West Germany. The paper’s findings present a threefold challenge to our existing understanding of guestworker politics. First, the design of each recruitment system had an independent impact on settlement outcomes. Second, normative and legal constraints on the enforcement of anti-settlement measures were not significant in the Swiss case, and only mattered during the West German post-recruitment period because they constituted a sudden departure from previous administrative practice. Third, in both countries, economic imperatives were important, but not deterministic, factors in accounting for mass recruitment. To explain the cross-national variation in settlement patterns, then, the paper argues that each guestworker system was fundamentally shaped by two factors. First, program design varied depending on whether or not political elites could draw policy lessons from past experience with temporary worker programs. Where past recruitment had resulted in unwanted settlement, as had been the case in Switzerland, political elites sought to adopt policy provisions designed to prevent the past from repeating itself. Where past policy failure was absent, as was the case in West Germany, policy makers were less concerned with pre-empting permanent immigration. As a second critical variable, program design reflected the degree to which policy makers were able to operate autonomously from cross-cutting interests. Whereas the West German government could pursue recruitment relatively insulated from both business and popular pressure, Swiss policy makers had to repeatedly accommodate both sets of actors.
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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 | 0.002 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".