The COVID-19 Crisis and the Global Compact on Migration: The Momentum and the Tool to Overcome “Immobility”
Bibliographic record
Abstract
After some forty years of security and closure discourse towards international migration, and of “crisis” management in the matter, the adoption of the Global Compact for Safe, Orderly and Regular Migration (GCM) in December 2018, although a non-legally binding instrument, seems to be able to lead to an overcoming of this approach, which is visibly counterproductive. Thus, states commit to “[e]nhance availability and flexibility of pathways for regular migration” (Objective 5) or “[s]trengthen certainty and predictability in migration procedures” (Objective 12). Through the idea of global cooperation, based on law, the ambition of states to adopt a more long-term approach to the migration phenomenon emerges. However, the COVID-19 crisis seemed to greatly diminish the prospects of implementing such an approach. The desire to limit the spread of the virus quickly led to a strict closure of borders, drastically reducing migration flows. The multilateral approach to this issue seemed to be quickly swept aside by national concerns: the initial reactions of the European Union Member States were a clear example. However, this borders closure, taken to its extreme for health reasons, has highlighted the migrant labour dependency of the economies of the “global North”. If the crisis stroke migration and international mobility in the short term, it is not certain that in the mid to long term, the consequences of the health crisis will not, on the contrary, lead to a reconsideration of the security and closure approach implied by crisis management. Indeed, the shortage of migrant workers, linked to the closures brought about by the COVID-19 crisis, has led to radical state responses — from the organisation of “charter” flights to facilitate the arrival of migrant workers to the regularisation of illegal workers or rejected asylum seekers — or, where states failed, to “underground” responses — labour migration having been managed mainly by the Mafia in Italy, for example. Therefore, it seems necessary to wonder if, in a way, the COVID-19 crisis, through the paroxysm of border closure it has brought and the needs it has thus underlined, could not constitute a momentum for the implementation of a new approach to international migration, as advocated by the GCM.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".