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Record W1911868656 · doi:10.26522/ssj.v4i2.1000

Constructing Citizenship Without a Licence: The Struggle of Undocumented Immigrants in the USA for Livelihoods and Recognition

2011· article· en· W1911868656 on OpenAlexvenueno aff
Fran Ansley

Bibliographic record

VenueStudies in Social Justice · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipFraming (construction)Human rightsImmigrationLegislaturePolitical scienceVictoryState (computer science)GlobalizationContext (archaeology)SociologySocial movementLawPolitical economyGender studiesPolitics

Abstract

fetched live from OpenAlex

This article questions the meanings and expression of "citizenship" in the context of new Latina and Latino migration into the southeastern United States-a region long marked by legally policed racial systems and now experiencing the varied shocks of globalization. Focused on a legislative campaign that won access to a state-issued driver's licence for undocumented migrants in Tennessee in spring 2001, the article explores some of the tensions that emerged on the road to this unlikely victory and raises questions for the immigrants' rights movement in the US about the costs and gains that may follow from different ways of framing its demands. The dominant frame this particular campaign adopted was a pragmatic and politically acceptable call to improve traffic safety, one that reflected a conscious choice to downplay issues of rights, justice or global perspective. Yet the article also reports that the campaign in fact created and used opportunities for activists to raise issues related to migrant rights. It also made a dramatic, albeit temporary, improvement in the daily lives of migrants in the state. The article then sketches three citizenship norms that current struggles might prefigure. These three norms are: the full right to international mobility of human beings; the right to identity; and duties of citizenship in a globalizing world.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.890

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 studies0.0010.001
Scholarly communication0.0000.000
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 head0.163
GPT teacher head0.407
Teacher spread0.245 · 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 designQualitative
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

Citations22
Published2011
Admission routes1
Has abstractyes

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