Exiting the fragility trap: evidence from Bangladesh
Bibliographic record
Abstract
This paper examines how diaspora can shape exits from state fragility. Potential linkages where effective policy reform might take place, include, but are not limited to, trade, direct investment and property rights. Understanding and explaining these linkages is important because much of the literature narrowly focuses on the importance and impact of diaspora remittances as a key input to reform and development. The purpose of this paper is to build on our prior research that examines six different linkages between diaspora and fragile states. Our focus is on host-homeland relations with a specific focus on the Canada-Bangladesh bilateral relationship. The paper unfolds in four sections. First, we compare and synthesize existing knowledge on understanding diaspora strategies in host-states using ideas on positionality and alignment. Second, we consider the conditions under which diaspora are likely to influence home-state policy. Third, to illustrate and demonstrate the utility of this comparative framework, we examine empirical evidence in support of the six linkages that shape Canada – Bangladesh diaspora relations. We have chosen Bangladesh as our case study because, despite regional conflict, civil war and entrenched poverty the country has moved to middle income status and can be considered to have exited fragility. In the fourth section, we conclude by focusing on implications for further research and policy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".