Turning Point in the Evolution of Soft Financing: The United Nations and the World Bank
Bibliographic record
Abstract
Half a century age, a drawn-out and divisive debate that lasted almost a decade (1949–59) took place at the United Nations (UN) over a proposal to set up a Special United Nations Fund for Economic Development (SUNFED) to provide soft financing for the development of the UN and the World Bank, as it came to be known, in the economic development of developing countries. Had the proposal been approved, it would have given the UN decisive leadership. In the event, opposition mainly from the United States but also from the International Bank for Reconstruction and Development (IBRD) led to the establishment of the International Development Association (IDA), the soft financing arm of the World Bank, in part as a foil to stop SUNFED, greatly strengthening the Bank's role at the expense of the UN. This account of what took place is based largely on archival material in the UN Archives and Records Centre in New York, the two histories of the World Bank, and the role played by a senior member of the UN Department of Economic Affairs, Hans (now Professor Sir Hans) Singer, who was intimately involved in the attempt to establish SUNFED.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".