Constraints on provincial and municipal borrowing in Canada: markets, rules, and norms
Bibliographic record
Abstract
Abstract: A common concern with fiscal decentralization has been the increased risk of macroeconomic instability. Sub national governments may behave in a fiscally irresponsible fashion. Central governments may feel obligated to bail out insolvent lower‐tier governments. Control over the fiscal tools needed for macroeconomic management may be lost. However, if the basic political and economic incentives facing decision‐makers are correctly structured, prior controls may not be needed. Canada offers a clear example of the strength of market and political budget constraints in the face of very soft ‐ indeed, non‐existent ‐ hierarchical constraints at the provincial level. However, Canada also offers an equally clear example of almost the opposite in the highly controlled and tightly constrained world of local government. These constraints were developed as a response to historic fiscal crises, with some modification since. Both systems were largely effective in coping with recent crises. Countries, like individuals, may learn from experience and inculcate norms of behaviour that constrain their actions even when none of the more obvious forms of hard budget constraint would seem to be applicable at the margin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".