Bibliographic record
Abstract
As the Global Financial Crisis deepened into late 2008, liquidity continued to deteriorate in Canadian credit markets. Canadian financial institutions curtailed their lending, which increased funding costs and reduced market-wide liquidity. In response, the Bank of Canada (BoC) took extraordinary measures to provide liquidity to financial market participants and improve credit conditions. On November 12, 2008, the BoC established the Term Loan Facility (TLF) to extend credit at a penalty rate for terms of approximately one month. The TLF was available to 14 major banks that were direct participants in Canada’s payments system, the Large Value Transfer System. Participants could pledge their relatively illiquid, CAD-denominated non-mortgage loan portfolios in exchange for central bank funding. The BoC established two other liquidity facilities during this period, the regular Term PRA and the Term PRA for Private Sector Instruments. The BoC conducted 50 auctions through the TLF, of which just seven were subscribed. In total, it auctioned CAD 5.2 billion (USD 4.2 billion) in funding. The limited participation in the TLF may suggest that Canadian financial institutions were able to obtain short-term funding from other, more cost-effective sources. The TLF expired on October 28, 2009, along with the Term PRA for Private Sector Instruments.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.191 | 0.026 |
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".