Bibliographic record
Abstract
The concept of settlement balances is simple in its definition but complex when attempting to understand how they interplay between a central bank and the financial system. Settlement balances can be defined as interest-bearing deposits that belong to participants of Canada’s payment system and that are an integral part of the high-value payment system. In response to the economic shock caused by the COVID-19 pandemic, the Bank of Canada undertook a range of extraordinary policy actions to provide exceptional liquidity to support the economy and ensure a stable and efficient Canadian financial system. The Bank’s extraordinary policy actions led to a rapid increase in the Bank’s asset holdings, followed by a corresponding increase in liabilities—mostly in the form of settlement balances. In fact, settlement balances grew by more than 1,500 times their pre-pandemic amount. The significant increase in settlement balances has piqued public interest; people want to better understand them. More specifically, people want to explore how settlement balances are created and the Bank’s role in that process, and what effects elevated levels can have on the Bank’s balance sheet and the financial system more broadly. This paper deconstructs settlement balances into four key concepts. It also provides historical context, explores the current state of settlement balances at the Bank of Canada, explains the factors that will influence their future evolution, and looks at the regulatory impacts of some of the actions taken. This work seeks to broaden the public’s understanding of the Bank’s role in promoting a safe and stable financial system in Canada.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".