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Record W2093617412 · doi:10.1177/1368431006068760

Cosmopolitan Justice and Immigration

2007· article· en· W2093617412 on OpenAlexaff
Omid A. Payrow Shabani

Bibliographic record

VenueEuropean Journal of Social Theory · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSociologyImmigrationPolitySovereigntyGlobal justiceJurisdictionLawGlobalizationPoliticsRefugeePolitical economyObligationPolitical scienceLaw and economics

Abstract

fetched live from OpenAlex

The pressures of globalization have resulted in shrinking distances and increased contact among people, rendering state boundaries and jurisdiction insufficient to deal with claims of justice exclusively. This challenge requires that we move beyond the limits of statism in political theorizing and acquire a cosmopolitan approach. In this article, from a discourse theoretic perspective, I consider what cosmopolitan justice would entail for policy and law-making concerning immigration. It is argued that: (1) from a moral point of view we cannot consider the problem of migration solely from the perspective of the people of affluent countries and have to take into account the perspective of the refugees, asylum seekers, and immigrants; (2) the growing interdependency of global economies gives rise to a moral obligation to assist the immigrants with special duties devolving upon the First World as the result of the history of colonization; and (3) the immigration law ought to be integrated into higher, or constitutional, law-making. In doing so, the discourse theoretic approach decouples national sovereignty (territorial integrity) and democratic polity, overcoming the problem of prioritization of geography over claims of membership.

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)0.007
metaresearch head score (Gemma)0.007
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0110.055
Scholarly communication0.0090.006
Open science0.0010.009
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.044
GPT teacher head0.341
Teacher spread0.297 · 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

Citations10
Published2007
Admission routes1
Has abstractyes

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