Spaces of Solidarity and Spaces of Exception: Migration and Membership During Pandemic Times
Bibliographic record
Abstract
During 2020, as the coronavirus pandemic spread around the world, we have witnessed countries making unprecedented decisions, restricting international travel and closing borders but also chartering fights to bring in migrant workers employed in essential sectors. While important (internal) travel restrictions were frst implemented by China in late February 2020 on the Chinese New Year holiday, the relevance of borders in relation to controlling the pandemic became internationally visible when the United States banned EU citizens from entering the country on 14 March 2020 as Covid-19 cases and victims sharply rose in Italy and a number of other European countries. A sweeping closure of the EU external borders to all nonEU citizens was announced on 17 March 2020 – a rare occasion where EU citizenship had a tangible effect on all EU citizens’ livelihoods without being mediated by their national citizenship. That closure confrmed that EU citizens and their national governments felt they were closer together and in solidarity and interdependence under this pandemic although intra-EU border closures followed. Indeed, March 2020 saw the closure of borders between countries with very long and strong socioeconomic and political ties such as Canada and the US (a closure that is still effective at the time of writing in November 2020), or member states of the European Union with one another. Regional trade and migration within west Africa were also interrupted abruptly when, for instance, Nigeria closed its borders on 23 March after recording its frst death from the virus. And while it was initially hoped that the summer of 2021 will bring not only temporary relief but also a way out of the pandemic, it has since become clear that 2020–2021 will be marked with at least selective border closures and migration and mobility restrictions. The wider impact of the pandemic on society and the economy will be long lasting and global.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".