Local Government Restructuring: Privatization and Its Alternatives
Bibliographic record
Abstract
Abstract Local government restructuring should no longer be viewed as a simple dichotomy between private and public provision. A 1997 survey of chief elected township and county officials in New York shows that local governments use both private and public sector mechanisms to structure the market, create competition, and attain economies of scale. In addition to privatization and inter‐municipal cooperation, two alternative forms of service delivery not previously researched—reverse privatization and governmental entrepreneurship—are analyzed here. Logistic regression on the 201 responding governments differentiates the decision to restructure from the level and complexity of restructuring. Results confirm that local governments are guided primarily by pragmatic concerns with information, monitoring, and service quality. Political factors are not significant in the restructuring process and unionization is only significant in cases of simple restructuring (privatization or cooperation used alone). Fiscal stress is not a primary motivator, but debt limits are associated with more complex forms of restructuring. Restructuring service delivery requires capacity to take risks and is more common among experienced local officials in larger, higher‐income communities. Restructuring should be viewed as a complex, pragmatic process where governments combine public and private provision with an active role as service provider and market player. © 2001 by the Association for Public Policy Analysis and Management.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".