Micro‐Level Determinants of Remittances from Recent Migrants to Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Despite considerable interest in remittances to developing countries, the limited availability of large sample data has constrained aspects of our understanding of remitter behaviour. This paper utilizes data from Statistics Canada’s Longitudinal Survey of Immigrants to Canada (LSIC) to investigate how the demographic characteristics of recent immigrants influence their remittance levels shortly after their arrival in Canada. We identify several hypotheses and use a Tobit model to estimate the impact of individual characteristics on remittance choices. As expected, remittances rise with incomes, age and falls with the size of the migrating family, housing costs and education. We also estimate how remittances are affected by other characteristics, such as gender, marital status, religion, region of origin, region of settlement and attitudes towards home and host communities.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it