14 The IMF and the Poor: Soft Loans, Hard Adjustment
Bibliographic record
Abstract
spring of 1985, one of his top priorities was to lower the barrier of formality that limited the ability of ministers to interact and to introduce and develop fresh ideas. For his first meeting, on April 17, he suggested that after the morning session, in which each member would make his traditional formal statement covering the main agenda items, the afternoon would be devoted to an informal exchange of views with restricted attendance. To give some structure and guidance to the discussion without restricting it to a set agenda, he asked Michael Wilson (finance minister of Canada) and V.P. Singh (finance minister of India) to outline a few key issues that members might address. In the morning session, Singh had raised the issue of the need to provide additional financing for low-income countries, stressing the role that an allocation of SDRs could play in that regard along with increases in official development assistance, Fund quotas, World Bank capital, and IDA (International Development Association) resources. In this less formal setting, he decided to toss another idea onto the table: over the next few years, the Fund would be receiving some SDR 3 billion in repayments from the Trust Fund
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".