Bibliographic record
Abstract
Abstract. This study is the first to investigate theoretically and empirically the determinants of Diaspora Bonds for eight developing countries (Bangladesh, Ethiopia, Ghana, India, Lebanon, Pakistan, the Philippines, and Sri-Lanka) and one developed country - Israel for the period 1951 and 2008. Empirical results are consistent with the predictions of the theoretical model. The most robust variables are the closeness indicator and the sovereign rating, both on the demand-side. The spread is not significant, suggesting Diaspora Bonds differ from normal investments. Good governance and wars are also important demand-side determinants of Diaspora Bonds. Among the supply-side factors; FDI, ODA, foreign exchange, inflation, external debt and remittances significantly determine the issue of Diaspora Bonds. Most importantly, this study is able to make predictions of the most promising candidate countries in issuing Diaspora Bonds in the future. Keywords. Diaspora Bonds, Supply-side, Demand-side. JEL. F21, F24, F34, F35, G38, H62, H63.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.044 | 0.020 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".