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Record W4389794844 · doi:10.35502/jcswb.354

Migrant minors in detention: Practical needs and the limits set by the European Convention

2023· article· en· W4389794844 on OpenAlexvenueno aff
Mattias Hjertstedt, I. M. Nilsson, Jonas Hansson

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
FundersVetenskapsrådet
KeywordsConventionLawScope (computer science)Human rightsPolitical scienceMinor (academic)Best interestsState (computer science)

Abstract

fetched live from OpenAlex

State officials report practical needs to put migrant minors in detention, and the European Convention on Human Rights sets legal limits on this practice. This article defines the scope of circumstances under which migrating minors may be detained by analyzing The European Court of Human Rights case law, using judgments in which the detention of migrant minors has been alleged a violation of Articles 3, 5.1, or 8. It also explores states’ needs for detaining such children, using data from 19 interviews with Swedish police officers, and compares these views with the case law. Police interviewees primarily describe two needs to detain children: to make deportations of children smooth and dignified, and to prevent minors from committing crimes. The investigation finds that migrant minor detentions are rarely permissible according to the Convention—especially under Article 3—and that the permissible scope is too small to meet the expressed practical needs. The actors involved in the issue of detaining migrant minors might have different perspectives on the issue, but they must not lose sight of the fact that these children are categorized as some of the most vulnerable in society and that their rights must be protected.

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)0.018
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.016
Scholarly communication0.0060.008
Open science0.0010.010
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.334
Teacher spread0.297 · 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 designQualitative
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

Explore more

Same venueJournal of Community Safety and Well-BeingSame topicMigration, Health and TraumaFrench-language works237,207