Governing migration: political contestation and policy formation
Bibliographic record
Abstract
Over the past decades, Europe has turned into a “continent of immigration” that is increasingly shaped by cross-border mobility and a growing ethno-cultural diversity of its population. The end of the Cold War divide in 1989, the completion of the single European market, and the accelerating speed of globalization have been the main driving forces behind this development. Clearly there is variation in this trend across Europe, but in general terms, Europe now shares some of the experiences of larger and sustained levels of immigration that are characteristic of immigrant settler states such as the United States or Canada. With the gradual, yet momentous, sociopolitical transformation of European societies through migration, the governance of cross-border movement, settlement, and cultural diversity poses some fundamental political and policy challenges. The politicization of “them” and “us” The most obvious of these challenges arises at the interface between political contestation and identity politics. Migration has become a highly contested and emotionally charged issue that has transformed competitive party politics in Western democracy. In particular, nationalist-populist actors have made migration and the protection of national borders the central theme around which their political mobilization revolves. Anti-migrant rhetoric and the scapegoating of migrants have become the cornerstone of right-wing populist ideologies across Europe. The resurgence of exclusionary nationalism and anti-immigrant forces has the potential of developing into a veritable threat to the viability of liberal democracies in general and the rights of minorities in particular. The politics of migration is likely to continue dominate electoral agendas and public debates on citizenship, identity, belonging, and community. In this respect, the politicization of migration is critically tied to a wider public debate about the transformation of traditional collective identities that historically have been shaped by legacies of colonialism and images of ethno-cultural homogeneity. It is a short step from identity politics to the racialized exclusion of migrants. The xenophobia, scapegoating, and populist backlash linked to the refugee crisis, ongoing refugee movements, and demographic change has a strong racialized and racializing element, particularly when it comes to characterizing Muslims
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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 | 0.027 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.025 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".