Bibliographic record
Abstract
Many independent countries in the Caribbean have large stocks of migrantsabroad who send remittances to relatives in the homeland. Most Caribbean migrants reside in four principal host or remittance-sending countries: the United States, Canada, the United Kingdom, and The Netherlands. Remittances constitute more than 5 percent of GDP in most countries and exceed 10 percent in the case of Guyana, Haiti, and Jamaica. The transfers help to smooth consumption patterns, alleviate poverty, increase the supply of investable funds, and improve balance of payments. Many developing countries and most Caribbean states are fiscally constrained and have limited access to private international capital markets. Given substantial remittance inflows worldwide (US$413 billion in 2014 compared with US$135 billion in foreign aid in the same year), the governments of remittance-receiving countries wonder how some of its migrants' savings could be tapped to bridge financing gaps. One means would be for governments to issue a diaspora bond with a submarket rate of return that targets patriotic migrants who want to help their home country grow and prosper. This paper defines diaspora bonds, discusses their performance in the post-World War II era, and reviews the critical steps and conditions for successful issuance and subscription. The characteristics of selected Caribbean states are then analyzed to determine suitability for possibly using this financial instrument.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".