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Record W4381167541 · doi:10.1177/01979183231181564

Remittance Modality: Unpacking Canadian Money Transfer Mechanism Choices

2023· article· en· W4381167541 on OpenAlexafffundabout
Samuel MacIsaac

Bibliographic record

VenueInternational Migration Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRemittanceUnpackingContext (archaeology)Informal sectorBusinessDeveloping countryCashPopulationEconomicsEconomic growthFinanceSociologyGeography

Abstract

fetched live from OpenAlex

Numerous international development targets aim to encourage and formalize remittances, because they can support development efforts while controlling and monitoring illicit capital flows. Despite continued efforts to promote formal remittance channels, informal remittances flourish among specific population groups. This study uses data from the Canadian Study on International Money Transfers to analyze the determinants of remittance modality or channel choice. Previous empirical work tends to classify remittances as either formal or informal. In contrast, this article considers a variety of channels. It shows that the dichotomization of formal versus informal remittances masks crucial differences across remittance channels. Due to Canada's unique geographical positionality, cash transfers operate distinctly from informal methods despite often being treated as a homogenous group in other studies. Interestingly, remitters are also more likely to use formal money transfer operators (most of which offer cash pickup options to recipients) than informal channels to send funds to countries with larger informal sectors. Within the context of Canadian remittance outflows, this invalidates the frequent assumption that more informal destination country economies push remitters to opt for informal transfer methods.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.338
Teacher spread0.304 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2023
Admission routes3
Has abstractyes

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