Toward Fiscal Sustainability in Thai Local Government: Lessons Learned from Local Fiscal Management Practices in Canada, France, Japan, South Korea, and United States
Bibliographic record
Abstract
This paper compares and assesses the fiscal management strategies used by local authorities in Canada, France, Japan, South Korea, and the United States. This comparative survey seeks to identify the salient attribute of each country’s local fiscal policy and demonstrates how Thai local governing bodies can adopt those strategies to improve their overall fiscal health. This paper suggests that fiscal sustainability in Thai local government can be attained by embracing each system’s core principle. The Thai national government could follow the Canadian federal government example by devolving more administrative responsibilities to local government units, while maintaining several essential regulatory functions, such as rectifying regional disparities and imbalances in public service provision. Following the Japanese model, Thai local government ought to diversify their revenue sources to ensure adequate funding for public service delivery and community development. The Korean case demonstrates that a carefully designed property tax system can raise a substantial amount of revenue for local government and prevent speculative land ownership. The American system emphasizes citizen participation and fiscal transparency (i.e., traceability of how tax money is levied, collected, and spent). The French model resolves potential conflict of interest issues among local elected officials by charging the highly skilled professionals with policy analysis, financial auditing, and accounting."
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".