Constructing Citizenship Without a Licence: The Struggle of Undocumented Immigrants in the USA for Livelihoods and Recognition
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".