MétaCan
Menu
Back to cohort
Record W2791384855 · doi:10.26522/ssj.v11i2.1509

Defending Family Unity as an Immigration Policy Priority

2018· article· en· W2791384855 on OpenAlexvenueno aff
Michael J. Sullivan

Bibliographic record

VenueStudies in Social Justice · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationDisadvantagedSolidarityImmigration policyArgument (complex analysis)SociologyCompetition (biology)Immigration lawPolitical scienceEconomicsPolitical economyLawPolitics

Abstract

fetched live from OpenAlex

In this article, I make a policy argument in defense of family and relationship-based immigration preferences in U.S. immigration law that accounts for economic objections and calls for solidarity among socioeconomically disadvantaged U.S. residents on this issue. I begin with a historical account of policy arguments for limiting family-based immigration. I challenge the view that family-based immigration is a fiscal burden on the nation as a whole and acts against the interests of disadvantaged native-born workers. Then, I present and respond to perception-based objections to family-based immigration by disadvantaged citizens who believe that they are suffering from competition with mixed-skilled immigrants, including those sponsored by family members. Advocates of family unity in immigration policy are fighting the perception of zero-sum competition between immigrants and disadvantaged citizens by organizing together for improvements in wages and working conditions, leveraging arguments from the U.S. civil rights struggle to advocate for inclusive immigration policies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0230.036
Scholarly communication0.0090.013
Open science0.0020.014
Research integrity0.0120.018
Insufficient payload (model declined to judge)0.0050.001

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 head0.076
GPT teacher head0.454
Teacher spread0.379 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations14
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueStudies in Social JusticeSame topicMigration and Labor DynamicsFrench-language works237,207