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Record W2973636331 · doi:10.5040/9798216006565

Refugees and Asylum Seekers

2019· book· en· W2973636331 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeForced migrationHuman rightsPolitical scienceImmigrationAsylum seekerDisplaced personComprehensive Plan of ActionRefugee lawScholarshipInclusion (mineral)LawSociologyGender studies

Abstract

fetched live from OpenAlex

This volume engages human rights, domestic immigration law, refugee policy in the United States, Canada, and Europe, and scholarship to examine forced migration, refugee resettlement, asylum seeker experiences, policies and programs for refugee well-being in North America and Europe. Given the recent "re-politicization" of forced migration and refugees in Europe and the U.S., this edited collection presents an in-depth, multi-dimensional analysis of the history of policies and laws related to the status of refugees and asylum seekers in the U.S., Canada, and Europe and the challenges and prospects of refugee and asylum seeker assistance and integration in the 21st century. The book provides rich insights on institutional perspectives critical to understanding the politics and practices of refugee resettlement and the asylum process in the U.S., Canada, and Europe, including international human rights and humanitarian law as well as domestic laws and policies related to forced migrants. Issues addressed include social welfare supports for resettled refugees; culturally responsive health and mental health approaches to working with refugees and asylum seekers; systemic failures in the asylum processing systems; and rights-based approaches to working with forced migrant children. The book also examines policy developments and strategies to advance the well-being and social inclusion of refugees in the U.S. and Europe.

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)0.001
metaresearch head score (Gemma)0.002
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.033
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0330.008

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 head0.015
GPT teacher head0.315
Teacher spread0.300 · 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
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

Citations44
Published2019
Admission routes1
Has abstractyes

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