Unanticipated Defaults and Losses in Canada's Large-Value Payments System, Revisited
Bibliographic record
Abstract
Recent work at the Bank of Canada studied the impact of default in Canada’s large-value payments system, and concluded that participants could readily manage their potential losses (McVanel 2005). In an extension of that work, the authors use a much larger set of daily payments data – with three times as many observations – to examine the simulated losses of private sector participants and the Bank from defaults in the payments system. They also gauge the upper bound of possible losses in the period April 2004 to April 2006. The authors conclude that losses from a participant failure in the large-value payments system are very likely to be small and readily manageable, as in McVanel (2005). For one or two small participants, under some (probably extreme) conditions, losses could be significant, but not solvency threatening. In sum, the risk controls of the large-value payments system allow and encourage participants to keep potential losses manageable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".