Bibliographic record
Abstract
This paper discusses how the bankers’ acceptance (BA) market in Canada is organized and its essential link to the Canadian Dollar Offered Rate (CDOR). Globally, BAs are a niche product used only in a limited number of jurisdictions. In Canada, BAs provide a key source of funding for small and medium-sized corporate borrowers that may not otherwise have direct access to the primary funding market because of their size and credit ratings. More recently, BAs have also become an increasingly important funding source for large corporate borrowers because of credit-rating downgrades in certain sectors and industry consolidation. With the market’s continued growth, BAs account for the greatest portion of money market instruments issued by non-government entities and are the second-largest money market instrument overall in Canada, averaging just over 25 per cent of the total domestic money market in 2017. For the investment community in Canada, BAs provide a source of short-term income and liquidity because of their relatively attractive yield, liquidity and credit ratings.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.005 |
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".