Bibliographic record
Abstract
While central banks cannot provide complete foresight with respect to their future policy actions, it is in the interests of both central banks and market participants that central banks be transparent about their reaction functions and how they may evolve in response to economic developments, shocks, and risks to their outlooks. This paper outlines the various ways in which the Bank of Canada seeks to explain its economic outlook and monetary policy decisions, with an emphasis on how different sources of uncertainty factor into monetary policy communications. To help markets and others understand its reaction function, the central bank must explain what uncertainties are weighing on policy and how (or if) these uncertainties are being considered in policy formulation. Discussion of uncertainty becomes particularly important when a large shock has hit the economy or when a central bank’s view or its policy stance is changing. Market views and the views of the central bank will not always be aligned. The aim of monetary policy communications should not be alignment but understanding—helping markets comprehend the central bank’s policy objectives and providing a coherent rationale for policy decisions. In doing so, the bank must be transparent about the uncertainties influencing the outlook, their possible impacts and how these uncertainties will be factored into policy decisions. This paper outlines some recent and upcoming initiatives to achieve those objectives and improve Bank of Canada communications.
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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 | 0.020 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".