The Road Ahead for Canadian Inflation Targeting
Bibliographic record
Abstract
1When I was asked to write a paper for this conference, I must admit to having less than my usual enthusiasm. Quite apart from the fact that I was unfamiliar with the large and growing literature on inflation targeting, my sense was that there were few unresolved issues. Canadian inflation targeting had been in place for 14 years, and it was widely viewed as a successful approach to monetary policy. Whatever issues remained were likely to be less interesting than the ones in the late 1980s, when the battle over “price stability ” was first joined, or the ones in the early and mid-1990s, when the main challenges were how to implement and communicate an effective inflation-targeting regime. A few months of reading changed my view. I was right that the remaining policy issues and related debates are less exciting than the ones from 15 years ago, and this is probably a good thing. But several interesting issues still need to be resolved, and there is still scope to improve inflation targeting in Canada. This paper offers my assessment of Canada’s inflation-targeting regime, as well as my view of some outstanding issues. The current Canadian inflation targets expire at the end of 2006; this expiration leads to the following three questions, which motivate the paper:
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".