Bibliographic record
Abstract
This paper provides a non-technical introduction to monetary policy—what it is, how it works, and why it matters. It discusses inflation volatility and why this is damaging to the economy, as well as why increased stability of output growth is desirable. In both cases, changes in Canadian economic performance over the past few decades are examined. The paper also provides a detailed discussion of the transmission mechanism for monetary policy and of the types of uncertainty that central banks must face in their conduct of monetary policy. Finally, the paper describes the types of information that central banks need in order to conduct monetary policy prudently. __________________________________________________________________ This paper expresses my own views about monetary policy and should not be interpreted as representing the official view of the Bank of Canada. It was written while I was the visiting Special Adviser at the Bank of Canada (September 2004 to August 2005). I would like to thank the Bank for this opportunity, as well as the many individuals who gave helpful comments on successive versions of the paper. All remaining errors are mine. Why Monetary Policy Matters A Canadian Perspective
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".