New Bottle, Old Wine? Implications of the World Bank's Systematic Diagnostic Reports for the Rise of Noncommunicable Diseases in the Organization of Eastern Caribbean States
Bibliographic record
Abstract
Since its foundation in 1944, the International Bank for Reconstruction and Development, soon renamed the World Bank (hereafter, "the Bank"), has shifted its initial goal of reconstructing post-World War II Europe to promoting economic development and alleviating poverty in Africa, Asia, Latin America, and the Caribbean. Bank assistance is provided through loans awarded to countries that agree to policy changes that the Bank deems necessary to achieve its stated goals. Bank policies-dubbed Structural Adjustment Programs (SAPs) and, over time, superseded by Poverty Reduction Strategy Papers-have been criticized for not only failing to ameliorate underdevelopment and poverty, but for exacerbating both. The most recent Bank approach to development and poverty alleviation, the Systematic Diagnostic Reports (SDR), attempts to address these criticisms. We appraise the SDR for six Organization of Eastern Caribbean States (OECS). All share daunting economic and social challenges, including an ongoing epidemic of noncommunicable diseases (NCDs). We argue that, contrary to the Bank's claims, these challenges will continue under, and may even be exacerbated by, the policies the Report demands, and we elaborate on their implications for NCDs in the OECS and for social and health equity in the region.
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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 | 0.068 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".