Bibliographic record
Abstract
Sustaining High Performing Public Enterprises presents steps taken by National Water and Sewerage Corporation of Uganda, a typical public enterprise, to sustain a high performance momentum after over 15 years of successful utility reforms. Specifically, the author pinpoints key achievements during the period 2013–2018 including growth in geographical coverage from 23 to 240 towns, increase in connections from 310,000 to about 600,000; revenues growing more than three times and network growth improving from 80kms per year to over 2000kms per year. The concept of new public management (NPM) is used to set the scene for a case description of various initiatives and innovations implemented. A balanced scorecard framework is used to characterize the various activities. It highlights a shift from over-emphasis on positive cash-flows alone to a balanced approach to ‘water for all’ citizens. The need to balance technical work and political aspirations is highlighted. Also featured is the nexus between utility operations and environmental protection to ensure sustainable water supply. The cardinal role of aligning staff needs to organizational needs and working for win-win solutions is also highlighted.Sustaining High Performing Public Enterprises presents strong lessons and conclusions for utility leaders and policy makers intending to reform their utilities to create value for citizens. It is also of value to academicians and researchers for scholarly studies in water and sanitation governance and management.Praise for Utility Benchmarking and Regulation in Developing Countries, also by Silver Mugisha:‘The performance of utilities, especially in developing countries, has continued to face daunting challenges. One of the challenges lies with adapting effective monitoring and regulatory systems to ensure successful implementation of strategic plans. Dr. Mugisha's book offers concrete tailor-made methods used to sharpen performance of utilities in developing countries. I highly recommend this book as a useful catalyst for utility performance reforms in developing countries.’ Dr. Michael McGarry, International Consultant in Performance Systems and Governance, Cowater International Inc., Canada.‘Dr. Mugisha's Book is a nice primer to my own book: Making Public Enterprises Work. It gives a more detailed analysis of the fundamentals and methods of designing practical performance monitoring systems. Reading Dr. Mugisha's book definitely enhances your deep understanding of ways of taming productivity in developing country-like utility settings.’ Dr. William T. Muhairwe, Managing Director, National Water and Sewerage Corporation, UgandaISBN: 9781789060249 (Hardback)ISBN: 9781789060256 (eBook)ISBN: 9781789060263 (ePUB)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".