The impact of state governance structures on management and performance of public organizations: A study of higher education institutions
Bibliographic record
Abstract
Abstract Legislative statutes are passed by political majorities which support structures that insulate the implementing agency from its political opponents over time. Political actors also respond to different constituencies. Depending on the broad or narrow base of these constituencies, actors favor different kinds of governance structures. We apply this theoretical framework to the question of whether the state governance structures of boards of higher education affect the way university managers allocate resources, develop sources of revenue, and promote research and undergraduate education. Over the past two decades state governments have given considerable attention to state governance issues, resulting in many universities operating in a more regulated setting today. This paper develops a classification of higher education structures and shows the effects of differences in these structures on university management and performance using a data set that covers the period from 1987 to 1998. The analysis suggests that, for most of the measures, productivity and resources are higher at universities with a statewide board that is more decentralized and has fewer regulatory powers. © 2004 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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".