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The International Organization for Migration (IOM): “Competent Structure” and “Inevitable Choice” for Russia and China to Affect Global Migration Governance?

2022· article· en· W4376634116 on OpenAlexaff
Martin Josef Geiger, Nadezhda Kokoeva, Yadi Zhang

Bibliographic record

VenueJournal of International Analytics · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsCarleton University
Fundersnot available
KeywordsCorporate governanceGlobal governancePolitical scienceChinaImmigrationRelevance (law)Development economicsLawManagementEconomics

Abstract

fetched live from OpenAlex

This article focuses on IOM and its place in global migration governance. China’s and Russia’s memberships were considered overdue, considering the relevance of both countries for the global migration system and their respective weight on the international stage. We aim to contribute to advancing research on IOM as an organization of increasing global relevance and on its engagement with member states, moving beyond the “usual” focus on the European Union (EU) member states, African, North American, and South American immigration and sending countries. Our analysis draws upon recent research, which conceptualizes intergovernmental organizations (IGOs) as “world organizations” and which we fi nd interesting and applicable to our empirical inquiry and discussion of IOM. We regard IOM as a “world organization” that could be examined along four interrelated components: (1) its “internal world” (e.g., establishment, relations with states, internal decisions); (2) its self-image and self-reference as an organization integrated into and referring to world society, hence as the “world of migration governance”; (3) its external relations, integration into wider environments, and responses to external events; and (4) its contribution to the world order, i.e., global migration governance. Our analysis shows that due to its new status as a related organization of the UN, its leading role in the Global Compact on Migration, and China and Russia becoming its new members, IOM will likely play an increasingly signifi cant role in global migration governance. The main reason for this is the need to reactivate the existing modes of migration governance and adapt them to a drastically changed global political and migration-related situation following the COVID-19 pandemic. Prior to their memberships in IOM, China and Russia have already been able to benefi t from the IOM assistance. Provided that both countries continue to engage with IOM and provide more substantial funding to it, IOM’s assistance to both China and Russia could be expanded. Meanwhile, both countries may take a position, which would allow them to exert a more signifi cant infl uence on IOM and global migration governance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.285
Teacher spread0.277 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations3
Published2022
Admission routes1
Has abstractyes

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