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Record W1751178648

Earned Citizenship: Property Lessons for Immigration Reform

2011· article· en· W1751178648 on OpenAlexaffabout
Ayelet Shachar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicConflict of Laws and Jurisdiction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCitizenshipLawEntitlement (fair division)PolityPolitical scienceLaw and economicsProperty (philosophy)Property rightsPoliticsSociologyEconomics
DOInot available

Abstract

fetched live from OpenAlex

At the heart of contemporary immigration debates lies a fundamental tension between the competing visions of "a nation of laws" and that of "a nation of immigrants." This is particularly evident in the American context. The nation-of-laws camp maintains that people who have breached the country's immigration law by entering without permission (or overstaying their initial visa) cannot overcome this "original sin," even if they have lived on its territory peacefully and productively for decades thereafter. The nation-of-immigrantsmilieu counters by reminding us that immigration is a vital component of the national self-definition of immigrant-receiving societies such as the United States, Canada, Australia, and New Zealand - the "flesh of our flesh," as noted historian Bernard Weisberger once put it.\nFor illustrative purposes, this Article will focus on the United States, which annually accepts the largest intake of immigrants in the world. No less significant, the United States is currently in the midst of an acrimonious debate over immigration reform. Canada, too, might see similar debates erupt in the future given the rise of temporary workers admissions that have skyrocketed in recent years.s If some of these temporary entrants remain beyond the terms of their initial visa, Canada might witness the establishment of a population that settles in the country for years yet remains prohibited from the protection of citizenship, for the regulations that govern the initial admission are specifically designed to bar the option of ascendance to citizenship and the fundamental protections (such as those against deportation) that come with it.

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)0.000
metaresearch head score (Gemma)0.000
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: Other · Consensus signal: none
Teacher disagreement score0.936
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.126
GPT teacher head0.339
Teacher spread0.213 · 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
GenreOther

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

Citations29
Published2011
Admission routes2
Has abstractyes

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