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Record W1969029342 · doi:10.2202/1565-3404.1158

Citizenship with a Vengeance

2007· article· en· W1969029342 on OpenAlexaff
Catherine Dauvergne

Bibliographic record

VenueTheoretical Inquiries in Law · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCitizenshipAmnestySovereigntyPolitical scienceLawImmigrationArgument (complex analysis)GlobalizationSociologyHuman rightsPolitics

Abstract

fetched live from OpenAlex

This Article situates contemporary shifts in citizenship law within a story of the relationship of globalization and illegal migration. The central argument is that citizenship as a formal legal status is enjoying a resurgence of authority at present. This mirrors the paradoxical nature of globalization itself: along the vector of citizenship, both inclusions and exclusions are increasing at present. As states are increasingly unable to assert exclusive power in a range of policy domains, immigration and citizenship law are transformed into a last bastion of sovereignty. Many shifts in citizenship law are explained through an understanding of how migration law and citizenship law work in tandem to form the border of the national community. Recent changes in citizenship law respond to two trends: a crackdown on extra-legal migration and a desire to reassert authority over diasporic populations. While the focus of the Article is on citizenship as a formal legal status, the importance of amnesty programs for extra-legal migrants demonstrates that ultimately the bifurcation of formal and substantive citizenship is untenable.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.029
Scholarly communication0.0070.008
Open science0.0010.008
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.016
GPT teacher head0.323
Teacher spread0.307 · 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

Citations52
Published2007
Admission routes1
Has abstractyes

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