Bibliographic record
Abstract
The goal of decentralized decision making is to ensure that local governments deliver services consistent with local preferences, make the most cost-effective use of tax moneys, provide fair governance, and are answerable to local residents. Structuring fiscal and institutional arrangements to achieve such diverse objectives for merit goods such as education, health, infrastructure, and poverty alleviation while supporting decentralized decision making is the motivation for various chapters in this section. Health and education expenditures constitute some of the most important public services that governments provide. Their features are also particularly relevant for nations with multiple orders of government. The provision of health care and education services, and sometimes health insurance coverage for individuals, is typically entrusted to subnational governments. At the same time these services fulfill important national objectives. They contribute to redistributive objectives such as equality of opportunity and social insurance, and they also promote efficiency and growth in the national economy. The result is that, although the provision of health and education services are decentralized, the federal government maintains an interest in how they are delivered and engages in policies to influence that delivery. Chapter 11 is devoted to investigating in more detail some the issues that arise because of this shared responsibility. It discusses conceptual considerations and practices in decentralized assignment of health and education services and financing mechanisms to ensure that equity objectives are not compromised in pursuit of efficiency and matching services with local preferences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".