The Global Economic Crisis and the Future of Migration: Issues and Prospects. What Will Migration Look Like in 2045? By Bimal Ghosh.
Bibliographic record
Abstract
Bimal Ghosh has worked as a senior director for the United Nations for many years, and was responsible for initiating a novel approach to migration under the label of ‘migration management’. In the early 1990s, his project on a ‘New International Regime for Orderly Movement of People’ (NIROMP) brought together some of today’s key stakeholders in the debate on migration management, including the International Organization for Migration (IOM) and other international institutions, in addition to states interested in identifying new solutions to ‘manage’ migration. The NIROMP project, devised on the basis of Ghosh’s recommendations in 1995 to the Commission on Global Governance, lobbied for a fundamental change in how to govern migration via a new ‘balanced’ and pragmatic approach. ‘Migration management’ also became rooted in the principle of developing a ‘regulated openness’ of states towards migrants and the goal of making migration work for all parties involved—not only for the benefit of the economy in receiving states but also for the development of countries of origin and the very individuals migrating to other countries. Besides his various appointments and achievements, one of Ghosh’s edited books (Managing Migration: Time for a New International Regime for Orderly Movements of People? Oxford: Oxford University Press, 2000) is a vivid testimony of his and other experts’ efforts to bring about this change in migration politics. More than a decade later, however, most of the recommendations made by Ghosh and other experts remain to be realized.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 0.015 |
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".