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Record W2800733456 · doi:10.1002/psp.2152

Strategic actions of transnational migrant parents regarding birth registration for stay‐behind children in Lombok, Indonesia

2018· article· en· W2800733456 on OpenAlexafffund
Leslie Butt, Jessica Ball

Bibliographic record

VenuePopulation Space and Place · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDocumentationContext (archaeology)State (computer science)Work (physics)Qualitative researchEconomic growthPolitical scienceBusinessGeographySociologyEconomicsSocial scienceComputer science

Abstract

fetched live from OpenAlex

Abstract Challenges to birth registration for children whose parents migrate transnationally for work have been inadequately investigated. Often a prerequisite to accessing state resources such as education and child protection, birth registration may meaningfully indicate a family's capacities to provide for children's well‐being. A multimethod qualitative study in 4 high‐migration communities in East Lombok, Indonesia, explored the strategic actions migrant parents take regarding birth registration. Families register children based on priorities, capacities, understanding of entitlements, and labyrinthine application processes. Three case studies describe the strategic actions families take with regards to registration to allow some measure of control over their children's well‐being: prioritising documents for adult migration; strictly conforming to registration requirements; and relying on false documentation. Findings suggest that parents' strategic actions result in limited family success in the context of weak state initiatives to link birth registration to valued resources for children and families.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.040
GPT teacher head0.324
Teacher spread0.284 · 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 designObservational
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

Citations12
Published2018
Admission routes2
Has abstractyes

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