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Record W3099135539 · doi:10.1002/psp.2409

Age of the oldest child and internal migration of immigrant families: A study using administrative data from immigrant landing and tax files

2020· article· en· W3099135539 on OpenAlexaff
Kate H. Choi, Sagi Ramaj, Michael Haan

Bibliographic record

VenuePopulation Space and Place · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of TorontoWestern University
Fundersnot available
KeywordsImmigrationCensusDemographic economicsGeographyPsychologyDemographySociologyEconomicsPopulation

Abstract

fetched live from OpenAlex

Abstract Immigrant parents report better opportunities for their children as the rationale for moving internationally. Residential mobility is associated with poorer outcomes for school‐age children. Many immigrant families prioritise child opportunities in their mobility decisions, especially those with school‐age children. These families may refrain from moving within the destination country to avoid such outcomes. Whether or not this is true is unknown because researchers have not examined how children's age shapes immigrant families' decisions to move within the host country. We link administrative immigration and income tax files with census data to examine how the age of the oldest child influences immigrant families' decisions to move after they immigrate. Immigrant families with school‐age children are less likely to move than those with younger children. Although the presence of older children deters migration for all immigrant families, those in immigrant gateways are more likely to move relative to those living in nongateways.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.069
GPT teacher head0.336
Teacher spread0.267 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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