Bibliographic record
Abstract
Bankers’ acceptances (BAs) are a form of investment security guaranteed by banks to fund loans to businesses against their credit lines. In Canada, BAs underpin the Canadian Dollar Offered Rate (CDOR), the main benchmark used to calculate floating interest rates in Canada’s derivatives market. In 2018, BAs formed the largest segment of money market securities traded in the secondary market at around CAD 35 billion (USD 26 billion) per week. When asset managers and the country’s public pension providers began shedding BAs amid the COVID-19 pandemic in early 2020, CDOR spiked, and the effects threatened to ripple throughout the Canadian financial system. On March 13, 2020, the Bank of Canada (BoC) established the Bankers’ Acceptance Purchase Facility (BAPF). The BAPF conducted multi-rate reverse auctions with Canadian primary dealers for highly rated BAs of remaining maturities up to 76 days. In its first two operations, dealers sold the BoC the total offered amounts of CAD 15 billion and CAD 20 billion, and the BA market stabilized. The BoC bought another CAD 12 billion of BAs in four operations in April. It continued to offer to buy CAD 10 billion in weekly, then biweekly reverse auctions until October, with no further bids from banks.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.292 | 0.049 |
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".