Poverty Reduction and Growth Trust―2020-21 Borrowing Agreements with The Government of Canada as Represented by The Minister of Finance, and The People’s Bank of China
Bibliographic record
Abstract
This paper presents two new borrowing agreements for the Poverty Reduction and Growth Trust (PRGT). These two agreements with the IMF, acting as Trustee for the PRGT, and the Government of Canada and the People’s Bank of China respectively have been finalized as part of the resource mobilization effort in response to the unprecedented demand for concessional financing driven by the COVID-19 pandemic and ensuing economic shocks. The fast-track loan mobilization round launched in April 2020 allowed the Fund to increase access limits and scale up emergency financing to low-income countries (LICs). To date, eleven new agreements and the augmentation of five existing agreements have been finalized with sixteen lenders (for previous updates see the October 2020 paper and the March 2021 paper. Together, these agreements and augmentations provide a total of SDR 16.9 billion in new PRGT loan resources for LICs, of which SDR 15.1 billion is immediately available.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".