Migration policy and the economy International experiences
Bibliographic record
Abstract
Inhaltsverzeichnis: Peter Stein: Preface (5-8); Ralph Rotte: Introduction - what can German migration policy learn from other countries? (9-16). I. Traditional immigration countries - Barry R. Chiswick, Teresa A. Sullivan: U.S.A.: the new immigrants (19-36); Don DeVoretz, Christiane Werner: Canada: an entrepot destination for immigrants? (37-56); Peter L. Muench-Heubner: Australia - a model of selected immigration (57-68); Rainer Winkelmann: Migration policy and socio-economic outcomes - New Zealand's experience with the point system (69-83). II. European experiences with immigration - Catherine Wihtol de Wenden: Migration policy and the economy - the French experience (87-100); Stefan Golder, Thomas Straubhaar: Migration policy and the economy - the case of Switzerland (101-118); Aslan Zorlu, Joop Hartog: Migration and immigrants - the case of the Netherlands (119-140); Timothy J. Hatton: International migration in Britain - trends and policies (141-156); Peter Huber: Migration and regions - the case of Austria (157-178); Michael Fertig, Christoph M. Schmidt: First- and second-generation migrants in Germany. What do we know and what do people think (179-218); Peder J. Pedersen, Nina Smith: International migration and migration policy in Denmark (219-237); Bjoern Gustafsson: Sweden's recent experience of international migration - issues and studies (237-259). III. Impact of migration in traditional emigration countries - Salvatore Strozza, Alessandra Venturini: Italy is no longer a country of emigration. Foreigners in Italy, how many, where they come from and what they do (263-280); Rossetos Fakiolas: Greek migration and foreign immigration in Greece (281-304); Francisco L. Rivera-Batiz: The international migration experience of Mexico - socio-economic aspects (305-318); Pawel Kaczmarczyk, Marek Okolski: From net emigration to net immigration - socio-economic aspects of international population movements in Poland (319-347)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".