Spaces of Solidarity and Spaces of Exception: Migration and Membership During Pandemic Times
Bibliographic record
Abstract
Abstract This chapter starts by introducing the policy and political context of the Covid-19 crisis, surveying some of the changes it brought to immigration policies in different countries: border closures for non-citizens; disruption for temporary migrants; and special arrangements for essential (migrant) workers like doctors and nurses or farmworkers to ensure emergency wards are staffed and the food processing chain is not disrupted. The chapter critically reviews these changes and discusses the main analytical and policy questions which the book addresses. It investigates how the pandemic forces us to rethink notions like membership, citizenship, belonging, but also solidarity, community, essential services or ‘essential’ workers. Migrants expose tensions and contradictions within these concepts and values. Citizens (who may carry the virus) cannot be banned from return to the homeland as they travel internationally or domestically; by contrast, temporary migrants or asylum seekers may be locked in their dormitories because of an outbreak in their midst to prevent spread and protect the citizens. This chapter shows that the specific tensions of the global pandemic for migration are linked to the more long-term tensions of globalisation, migration, and the nation-state, suggesting that the pandemic is but a magnifying lens. The chapter concludes with an overview of the book’s contents.
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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 | 0.001 |
| 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 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".