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Record W2474037147 · doi:10.1515/roe-2015-1004

Remittance Behaviour of Chinese and Indian Immigrants in Canada

2016· article· en· W2474037147 on OpenAlexaffabout
Murshed Chowdhury, Anupam Das

Bibliographic record

VenueReview of Economics · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsMount Royal UniversityAlgoma University
Fundersnot available
KeywordsRemittanceImmigrationDemographic economicsMarital statusInstrumental variableLogistic regressionInvestment (military)Ethnic groupGeographyEconomicsDemographyPolitical scienceSociologyEconomic growthMedicineEconometrics

Abstract

fetched live from OpenAlex

Abstract Using the Longitudinal Survey of Immigrants in Canada (LSIC) dataset, we explore the differences in remittance behaviour of Chinese and Indian immigrants in Canada in relation to their socio-economic characteristics. We apply logistic regressions on the likelihood to remit, and instrumental variable regressions to estimate the amount remitted. We find that age, income, level of education, and personal investment in the home country are important determinants of the remittance behaviour of Chinese immigrants in Canada. Marital status, having family members, and involvement with ethnic organizations in the host country are the major drivers of remittances sent by Indian immigrants in Canada. By analyzing the remittance behaviour of Canadian immigrants from two major sources, this study sheds light on potential motivations to remit which contribute to policy formulation in both home and host countries.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
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 head0.008
GPT teacher head0.256
Teacher spread0.248 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

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