Bibliographic record
Abstract
This Technical Note on Systemic Liquidity Assessment examines that confidence in the resilience of the Canadian financial system is strong but liquidity risks warrant attention. The financial system is strong thanks to the well-capitalized banking sector and the well-regulated financial system. It has also demonstrated resilience during past episodes of turmoil. The authorities should maintain efforts to enhance efficiency and liquidity in the domestic secured funding market. They should also monitor closely the ongoing structural shifts in the unsecured Canadian funding market and assess the merits of bolstering this market. Addressing data gaps and enhancing data-sharing and transparency of domestic funding markets are priorities. Despite progress in trade-level data for the repo market, a comprehensive market view is still missing. The Bank of Canada (BOC) should clarify the eligibility of the bilateral standing facilities and its emergency lending assistance policy, explicitly confirming that liquidity support is not restricted to recovery and resolution cases. In order to enhance responsiveness to evolving market risks, the BOC should increase the frequency of its collateral haircut calibration and its operational tests. Doing so would help ensure that its framework remains effective and adaptive.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.295 | 0.043 |
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".