Bibliographic record
Abstract
Abstract The Covid-19 pandemic has an impact on migrants’ return desires and actual returns across the globe. Border closures in the face of pandemic lead to the panic mobility of those returning home. The ensuing lockdowns and economic difficulties restricted migrant workers’ access to income and protection, pushing them to return. The pandemic brought evident risks for the regular migrants’ access to healthcare, financial security, and social protection, forcing them to consider the return option too. For irregular migrants, the pandemic further increased the risk of forced returns, including detention, deportation, and pushbacks. For all migrants, decisions are marked by a deep dilemma between staying and returning. Meanwhile, receiving, sending, and transit countries, as well as international organisations are involved in return processes by providing logistics, on the one hand, and stigmatising returnees as carriers of virus, on the other. This study is based on desk research and analysis of the scholarly literature, reports, and grey literature from international organizations, civil society reports, scientific blogs, and media reports. An emphasis on returns provides us broader insights to evaluate changing characteristics of migration and mobility in ‘pandemic times’, the governance of returns, its consequences, and the rhetoric about returnees.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".