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Immigration and Social Equality

2023· book· en· W4384932687 on OpenAlexaboutno aff
Désirée Lim

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationInjusticeCitizenshipPolitical scienceImmigration reformImmigration policyImmigration lawPolitical economySociologyLawPolitics

Abstract

fetched live from OpenAlex

Abstract Skill-selective immigration policies, through which states favor the admission of highly skilled migrants over low-skilled migrants, are a familiar component of the immigration landscape. Wealthy Western states, such as the United States, United Kingdom, Canada, and Australia, have explicitly declared their desire to attract the “best and the brightest.” On the other hand, attitudes toward low-skilled migrants could not be more different. They have consistently been portrayed as dangerous and undesirable, a drain on social welfare, and economically threatening to citizens. This book argues that we ought to rethink this stance. Beginning from the widely shared principle of equal respect for all persons, it proposes that equal respect requires the recognition of each person’s pro tanto right to social equality, regardless of their citizenship status. Even if states have the right to exclude noncitizens, they cannot do so in a way that is demeaning or subordinating to excluded persons. The right to social equality gives us a richer picture of why certain instances of immigrant selection, such as the United States’ recent ban on citizens from Muslim-majority countries, are unjust. However, it also has troubling implications for skill-selective immigration policies, as they are currently practiced: the book reveals that they ought to be regarded as a form of wrongful discrimination. Drawing on the framework of social equality, the book goes on to consider the problem of colonial injustice and how it may be reproduced by skill-selective immigration policies, as well as migratorial disobedience.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.616
Threshold uncertainty score0.860

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 studies0.0010.000
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 head0.044
GPT teacher head0.340
Teacher spread0.296 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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