Bibliographic record
Abstract
E-Governance is a powerful tool for bringing about change to government processes in the developing world. E-governance operates at the cross roads between Information and Communication Technology and government processes, and can be divided into three overlapping domains: e-administration, e-services and e-society. In order to be successful, e-governance must be firmly embedded in the existing government processes, must be supported, both politically and technically, by the governments, and must provide users with reasons to use these on-line domains. In order to maximize the impact, process change needs to be considered part and parcel of e-governance. In this report, we present and evaluate an e-governance programme in the East African country of Uganda. The programme, DistrictNet, tries to provide transparency at the local government level and to improve the provision of public information through the implementation of ICT. DistrictNet started in 2002 and is on-going. The achievements of the programme are presented and evaluated according to the criteria of the three domains of e-governance and their impact on government processes. On the basis of this evaluation, we elicit lessons that can be used to guide similar programmes at the local government levels in the developing world.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".