Bibliographic record
Abstract
THE CASE FOR DECENTRALIZATION In Chapter 2, we outlined the general principles of expenditure assignment and discussed in general terms the kinds of responsibilities that could be decentralized to the states. In this chapter, we consider expenditure assignment in more detail. The application of the general principles to specific types of expenditure functions is discussed, as well as some additional problems that arise in coordinating state provision of expenditure programs with national objectives. It might be worth briefly recalling and summarizing the key arguments for decentralization of expenditures to put the following discussion into context. The following arguments constitute the case for decentralizing expenditure responsibilities. These have also been briefly discussed in earlier chapters. Catering to Regional Preferences and Needs The classic argument for decentralization (Oates, 1972) is that different states have different demands for types and levels of public goods and services. This variation may simply come from personal preferences of the residents themselves, perhaps arising from cultural differences or other sources of heterogeneity across states. Or it may come from more objective factors such as geographic differences (e.g., terrain, population density), demographic differences (age structure of the population), or relative price or cost differences.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.090 | 0.019 |
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".