Policy Instruments and Budgetary Processes: A Reflection on the Deficit Elimination Experience in the Canadian Provinces
Bibliographic record
Abstract
Analyses of the deficit elimination experience in provincial governments in Canada in the 1990s show that provincial authorities used similar sets of policy measures to reach the goal of a balanced budget within a short period of time. We look at these measures as policy instruments and we try to make sense of their use through a theoretically informed reflection on the role of information and trust in the budgetary process. The principal-agent theory shows how the Premier and his team circumvented the problems caused by the information monopoly of managers through the implementation of a top-down process for setting budgetary targets and through the decentralisation of operational decision-making. However, this theory is completely silent concerning the use of rhetorical instruments. The convention theory suggests that regulatory and rhetorical measures were combined to influence guardians of the treasury and program advocates in their visions of the budget and thus changed the budgetary process from an incremental process to a fiscal crisis process. The same policy instruments take on a different meaning depending on the theoretical lens that one uses. Their use fosters identical outcomes (in this case, a balanced budget) through different paths. The knowledge of the process through which the use of a given policy instrument might lead to a given outcome is essential if we want to get a better grasp of the side effects of the use of any policy instruments.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.048 | 0.022 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".