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Record W4408380696 · doi:10.1177/01979183251325177

Theorizing Legitimacy in Migration Research

2025· article· en· W4408380696 on OpenAlexfundno aff
Nathan T.B. Ly

Bibliographic record

VenueInternational Migration Review · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLegitimacySociologyPolitical scienceEconomic geographyGeographyPoliticsLaw

Abstract

fetched live from OpenAlex

The concept of “legitimacy” can foster new insights and be of wide relevance to migration research: states seek to exercise “legitimate” power to regulate movement, organizations strive for “legitimacy” in their fields, and individuals want to occupy “legitimate” positions. The concept's usage, however, is largely isolated to specific contexts and cases. Those looking to engage it in their work face at least two challenges: (1) existing studies understand and apply legitimacy in different ways; and (2) there are no well-defined theoretical perspectives in the field to draw on. To facilitate such engagement, I first propose some shared understandings of legitimacy—namely a definition and conceptualization—that are widely applicable, amenable to diverse perspectives and approaches, and analytically useful. I then outline a theoretical perspective—one relating legitimacy to the actions of key players in migration (individuals, groups, organizations, states, etc.)—that can help researchers integrate legitimacy into their work, seek new avenues for future study, and bring the concept into wider conversations in the field. Finally, I illustrate how these arguments can enable new insights by expanding first on the theory of functional imperatives, followed by three substantive areas of migration research: the implementation and effectiveness of migration policies; the migration state and federalism; and status, deservingness, and social movements.

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)0.004
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.877
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.487
Teacher spread0.420 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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