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Record W2124453254 · doi:10.1093/jrs/fet038

The Global Economic Crisis and the Future of Migration: Issues and Prospects. What Will Migration Look Like in 2045? By Bimal Ghosh.

2013· article· en· W2124453254 on OpenAlexaffabout
Martin Josef Geiger

Bibliographic record

VenueJournal of Refugee Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsCarleton University
Fundersnot available
KeywordsPolitical scienceGeiger counterPoliticsRefugee crisisPolitical economySociologyEconomic historyLawHistory

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0020.003
Scholarly communication0.0040.009
Open science0.0010.004
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0360.015

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.

Opus teacher head0.006
GPT teacher head0.288
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes2
Has abstractyes

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Same venueJournal of Refugee StudiesSame topicMigration and Labor DynamicsFrench-language works237,207