From Money Targeting to Inflation Targeting: The Change in the Role of Money in the Conduct of Monetary Policy
Bibliographic record
Abstract
From the day of his arrival in 1975, David Laidler has made important contributions to both monetary theory and monetary policy in Canada. Of course, even before he came to Canada, he was well known by reputation to Bank of Canada staff, particularly from the first edition of his textbook on the demand for money (Laidler, 1969) and his involvement in the debate on the causes of inflation in the United Kingdom (Laidler and Parkin, 1975). But we were looking forward to interacting with him in person and hoping that he would become involved in the monetary policy debates in Canada. On both counts, David’s time in Canada has been a great success. And for me personally, it has been an enormous pleasure to discuss and debate issues of monetary theory and policy with David over the past 30 years, as well as to learn from him, and not infrequently to disagree with him. Initially, we met at academic meetings of various types, later at the C. D. Howe Institute, and subsequently during a very pleasant year when David was the first visiting Special Adviser at the Bank of Canada.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".