Bibliographic record
Abstract
The potential for ICT to positively contribute to good governance has been known for a long time and has been the subject of many articles and reports, but very little concrete empirical evidence of the effects of ICT on governance, and how these effects should be evaluated, exist. The situation is even worse when we consider ICT and governance in local governments. The case study reported in this paper is based on an e-governance outcome evaluation framework that is being followed by Local Governance and ICTs Research Network for Africa (LOG-IN Africa). This framework draws on existing literature on e-government, good governance, and results-based management. Data for the case study was collected through surveys, key informant interviews, focus group discussions and review of relevant documents in two municipal councils in Kenya. The focus was on the perspectives of consumers of the services provided by the councils. Data was analyzed using both qualitative and quantitative methods.The preliminary results show that the integrated financial management system implementation had modest improvements in most indicators of the following good governance constructs: participation, responsiveness, transparency, accountability, and efficiency and effectiveness. Given the modest improvements in good governance associated with the implementation of the system in the two municipal councils and the governance challenges in implementing a similar system in central government, the paper recommends, among other things, that local governments could be used to pilot complex e-governance initiatives and lessons learned used to scale up at national level.
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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 | 0.005 | 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 | 0.003 | 0.022 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".