Bibliographic record
Abstract
With Canadian banks curtailing their funding in response to the Global Financial Crisis, liquidity dried up in money markets and bond markets. On October 14, 2008, the Bank of Canada (BoC) announced its first Private-Sector Term PRA facility to provide liquidity to large money-market participants, such as asset managers, who were not traditional BoC counterparties and could not access the BoC’s other emergency liquidity facilities (“PRA” is short for purchase and resale agreement, similar to a repo). The program accepted commercial paper, asset-backed commercial paper, and bankers’ acceptances as collateral. It complemented the BoC’s Term PRA for primary dealers (the “regular Term PRA”), which the BoC extended to major banks on the same day. The BoC was prepared to allocate CAD 1 billion (USD 0.8 billion) to weekly auctions for the Private-Sector Term PRA, but only CAD 25 million (the minimum amount) was taken up in any single week. On February 23, 2009, the BoC replaced this facility with its second Private-Sector Term PRA. The second facility added investment-grade corporate bonds as eligible collateral and expanded the list of eligible participants. The two Private-Sector Term PRAs saw much less use than the regular Term PRA, ultimately peaking at about CAD 3 billion outstanding in mid-2009. The BoC allowed the facility to expire on October 27, 2009.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.003 |
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".