E-governance in Eastern and Southern Africa: a Webometric study of the Governments? websites
Bibliographic record
Abstract
This paper explores the adoption of one of the Information and Communication Technology (ICT) tools, i.e. the Internet and more particularly, the World Wide Web, by Eastern and Southern African governments as a means of facilitating interactions between the state and its citizens. It was observed that most governments in the region have constructed their own Web sites, some of which are up to date. English is the most com-monly used language to prepare the web sites. Other findings include: foreign missions recorded the highest number of web pages followed by political parties; the .com or .co Top Level Domain (TLD) generated most web pages followed by .ac or .edu in each country; most governments provide contact information as op-posed to sitemaps and feedback forms which recorded relatively few postings; governments with few web-pages and large quantities of in-links (including self-links) recorded high Web Impact Factors (WIFs); and only the South African government provided links to other Eastern and Southern African governments. Ethical issues regarding the analyzed variables as well as conclusions and recommendations are provided.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".