What is 'Credible' Fiscal Policy? The Canadian Experience, 1983-2010: The View of a Former Practitioner
Bibliographic record
Abstract
C. Scott Clark1 C. S. Clark Consulting I WAS ASKED INITIALLY TO CONTRIBUTE an article on “ideal” fiscal policy. I have no idea what “ideal” fiscal policy means and I doubt that any one else does as well. I decided, therefore, to replace “ideal” with “credible,” even though I also know there is no agreement among economists as to what constitutes “credible” fiscal policy. Lack of agreement among economists is a normal state of affairs. What I do know, however, is that I have heard and used the term “credible” fiscal policy on many occasions over the past 30 years, in numerous discussions with Canadian Ministers of Finance, in G-7 meetings of Finance Ministers and Central Bank Governors, and at meetings at the International Monetary Fund (IMF), the Organization for Economic Cooperation and Development (OECD) and the European Bank for Reconstruction and Development (EBRD).
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.036 | 0.025 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".