Bibliographic record
Abstract
Recent research has shown that monetary policy based on price-level targeting has several advantages over the traditional inflation targeting method, particularly in times of economic distress. Although several central banks have been coping with the aftershocks of the 2008 financial crisis for prolonged periods, none has adopted price-level targeting. This Commentary reviews some of the reasons for this in the Canadian and American contexts. The relative mildness of Canada’s 2008-2009 recession convinced the Bank of Canada that inflation-targeting can work in troubled as well as tranquil times. Meanwhile, the severity of the US recession led the Federal Reserve to explore several types of unconventional monetary policies, but not price-level targeting. The latter requires a commitment to offset the effects of unexpected inflation on the price level and makes monetary policy history-dependent. The Fed prefers to exercise discretion, and inflation targeting allows central banks to ignore past inflation shocks and engage in fine-tuning of the business cycle. The Bank of Canada shares the Fed’s predilection for discretion in this regard.
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 | 0.013 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.038 | 0.041 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".