Corporate Sustainability of Green Technology AndAssessment of The Environment And Challenges Faced byRegulatory Authorities in Uganda: A Case of The ElectricityRegulatory Authority (ERA)
Bibliographic record
Abstract
Governments across Africa have established regulatory agencies for utilities but these have largely been modeled on those in developed countries and have had limited success. The Electricity Regulatory Authority in Uganda was set up to oversee this important sector but its success has so far been limited as evidenced by the quality, reliability and cost of the service. The Purpose of this study is to examine the factors that influence the performance of ERA, with a view to coming up with mitigation measures. In the first part of the study (presented in this paper), annual reports of the regulatory body, supplemented with press reports, academic papers, and conferences reports are examined. In the second part of the study, the Electricity Governance Toolkit (EGTK) will be used to collect data from relevant electricity laws, rules and regulations developed by the Government of Uganda as well as procedures developed by the regulatory body, and the decisions it has made overtime. In addition, the researchers will conduct interviews with regulatory members and staff, civil society and consumer groups that have filed cases before ERA, as well as a sample of domestic and industrial consumers within Kampala District. Both qualitative (arranging the findings in themes) and quantitative techniques (t tests, frequencies and means) will be utilized to analyze the data.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| 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".