Bibliographic record
Abstract
1. Introduction: Sarah Spencer (Institute of Public Policy Research). 2. Migration to Europe Since 1945: Its History and its Lessons: Randall Hansen (University of Oxford). 3. Managing Rapid and Deep Change in the Newest Age of Migration: Demetrios G. Papademetriou (Migration Policy Institute, Washington DC). 4. The Economic Impact of Labour Migration: Mark Kleinman (University of Bristol). 5. Refugees and the Global Politics of Asylum: Jeff Crisp (Head of the Evaluation and policy Analysis Unit at the Office of the UN High Commissioner for Refugees). 6. The Closing of the European Gates? The New Populist Parties of Europe: John Lloyd (Financial Times). 7. Muslims and the Politics of Difference: Tariq Modood (University of Bristol). 8. The Politics of European Union Migration Policy: Claude Moraes MEP (Member of the European Parliament). 9. The Politics of US Immigration Reform: Susan Martin (Georgetown University). 10. Migration and the Welfare State in Europe: Andrew Geddes (University of Liverpool). 11. Understanding Anti--Asylum Rhetoric: Restrictive Politics or Racist Publics?: Paul Statham (University of Leeds). 12. Immigration and the Politics of Public Opinion: Shamit Saggar (Yale University). 13. Immigration, Citizenship, Multiculturalism: Exploring the Links: Will Kymlicka (Queen's University, Kingston, Ontario).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".