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Record W217412966 · doi:10.4324/9780203013564

Migration and Health in Asia

2006· book· en· W217412966 on OpenAlexfundno aff
Santosh Jatrana, Mika Toyota, Brenda S. A. Yeoh

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
FundersNational University of SingaporeYork UniversityHarvard University
KeywordsGeographyPolitical science

Abstract

fetched live from OpenAlex

The processes of migration and health are inextricably linked in complex ways, with migration impacting on the mental and physical health of individuals and communities. Health itself can be a motivation for moving or a reason for staying, and migration can have implications on the health of those who move, those who are left behind, and the communities that receive migrants. This volume brings together some of the increasing number of researchers who are studying health and migration in Asia - a continent which is a major exporter and importer of human resources. Using both quantitative and qualitative approaches, the essays included in this work investigate the interdisciplinary issues of health and health-related behaviours in the field of migration. Comprehensive and scholarly, Migration and Health in Asia also covers major themes such as the pandemics of HIV/AIDS and SARS, differential access to health and civil society for migrants, and the health of the populations who are left behind

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)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.002
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 head0.018
GPT teacher head0.314
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 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

Citations63
Published2006
Admission routes1
Has abstractyes

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