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Record W4412109757 · doi:10.1093/clp/cuaf008

Not Just in Outer Space: ‘Aliens’ in Immigration and Nationality Law

2025· article· en· W4412109757 on OpenAlexaboutno aff
Devyani Prabhat

Bibliographic record

VenueCurrent Legal Problems · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Aviation
Canadian institutionsnot available
Fundersnot available
KeywordsNationalityOuter spaceImmigrationLawSpace (punctuation)Political scienceImmigration lawPhilosophy

Abstract

fetched live from OpenAlex

Abstract The word ‘alien’ should be replaced with ‘non-citizen’ or ‘foreign national’ in UK case law and legislation. Legal transplantation of the term from England to other countries such as Australia and the United States which were part of the British Empire, and are largely populated by European settlers, resulted in departures from its original usage to mean non-subject. It was used to control foreign populations, as well as those already present long-term within these countries (including indigenous populations) in a deeply racialised manner took place. In the UK, exclusion of former colonised subjects from the British Isles was attempted by removal of subjecthood status and associated legal barriers to their entry and residence rather than through identifying aliens. While in present-day UK, statutory instruments do not use the term alien very often, in Australia it is a specific constitutional power. In the United States, the term alien is part of many statutes including those wholly unrelated to immigration or nationality. Yet in other similar Empire-linked settled jurisdictions which also borrowed the term from Britain, such as New Zealand and Canada, the word alien was dropped from modern immigration and nationality statutes altogether in order to avoid its pejorative connotations. The implication of this comparative analysis is that the word alien is far less entrenched in current UK law than it is in Australia or the US, and the UK may be able to adopt similar changes as New Zealand and Canada.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.034
GPT teacher head0.346
Teacher spread0.312 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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