THE THEORY AND EVIDENCE PERTAINING TO LOCAL GOVERNMENT MIXED ENTERPRISES
Bibliographic record
Abstract
ABSTRACT This paper addresses some of the gaps in both classification and theory pertaining to local government MEs and presents tentative predictions concerning the performance of local MEs. As a preliminary, we identify the different forms of entities with ME characteristics and place them within a comprehensive taxonomy. Most local MEs provide local public goods. Consequently, their primary goal should be to improve social welfare. This goal should drive both theory development and the evaluation of ME performance. We present three principal‐agent models that offer contrasting theories of ME performance with differing assumptions about the motivations and behaviour of the relevant actors: (1) a ‘best of both worlds’ model; (2) a ‘worst of both worlds’ model, and (3) a ‘profit collusion world’ model. We indirectly test these models by reviewing and assessing the empirical performance of MEs, focusing on their social welfare effects, or using related measures of performance where we have no direct evidence on social welfare effects. Finally, we draw on the theory and empirical evidence to make some predictions about the behaviour and performance of local MEs.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".