Performance Evaluation and Measurement in Public Organizations: A Systematic Literature Review
Bibliographic record
Abstract
The turbulence of the current competitive environment emphasizes the importance of the role played by performance measurement systems in generating an improvement of business results. Starting from this consideration, the work pursues a twofold goal: firstly, it tries to verify the existence and the degree of a research interest about this topic; secondly, it seeks to identify, in measurement and evaluation systems, which factors are capable of producing an effect on performances of public organizations. In order to well respond to the research purposes, the work begins with a systematic literature review, which highlights a growing attention of scholars on all those variables considered critical in conducting and managing public organizations. The study, highlighting the existence of six variables to be advantageously taken into account in managing public organizations, especially in light of the potential influence that they seem to exert on different types of business performances, could be considered as a useful tool for both practitioners (managers of public organizations) and scholars (professors, researchers, students, etc.) aimed at helping to become aware about the advantages arising from an adequate management of performances measures. The main research limitation is the lack of an empirical analysis of public companies performance plans, which should be thoroughly examined to allow a possible further generalization of the theoretical findings achieved.
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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 | 0.041 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.030 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".