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Record W7064658089

Comparative Immigration Policies for Unaccompanied Minors: A Shared Challenge

2023· article· en· W7064658089 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationHuman rightsEnforcementImmigration lawFace (sociological concept)Law enforcementFundamental rightsPhenomenon
DOInot available

Abstract

fetched live from OpenAlex

Unaccompanied minors from the Northern-Triangle and Mexico have been arriving at the United States border in large numbers over the past decade as a result of forced migration movements. Although the arrival of unaccompanied minors is not a new phenomenon in the United States, recent administrations have responded in ways that have made the country's immigration system increasingly hostile towards them.\nHowever, this issue is not exclusive to the United States. Unaccompanied minors traveling alone to Europe, Australia, South Africa, Canada, or the United States face similar dangers and are particularly vulnerable to abuse and trafficking. Regardless of jurisdiction, the treatment, care, and protection of the human rights of unaccompanied minors pose significant challenges. Around the world, unaccompanied minors are subject to similar human rights violations, and both international and domestic laws have proven to be ineffective in protecting them.\nAs long as countries prioritize the enforcement of their immigration laws, which are not designed to protect minors, the human rights and international standards of unaccompanied minors will continue to be violated as they migrate and seek asylum. It is crucial to recognize and address the unique needs and vulnerabilities of unaccompanied minors. Only then can we hope to ensure their safety and protect their fundamental human rights.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score1.000

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)0.0000.001

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 head0.039
GPT teacher head0.293
Teacher spread0.254 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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