Bibliographic record
Abstract
An estimated 9 million Filipinos, about 10% of the population, now reside abroad as permanent immigrants in advanced economies and as temporary migrant workers. The permanent immigrants who are mostly in the US, Australia and Canada comprise about 45% of the total. The migrant workers are employed in varied occupations in very varied destinations encompassing all the world continents. The migrants' remittances have contributed greatly to GNP and foreign exchange earnings averaging 6-7% and 20%, respectively, over the past decade. The paper analyzes the migrants' remittance behavior using individual observations from an Asian Development Bank 2004 survey of vacationing migrants. The paper assumes altruism to be the principal motivation for remitting foreign income and finds empirical support from the data. A two-stage regression model that estimates the effect of predicted foreign income, immigration status, (permanent immigrant or temporary worker) and demographic variables was run. Immigration status is found to be a significant explanatory variable for it determines the migrant's family location. Temporary workers tend to send proportionately higher income to the families they have left behind. Destination also matters for foreign income varies across destination, argued to be due to labor market segmentation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".