Assessing the Effectiveness of e-Government and e-Governance in South Africa: During National Lockdown 2020
Bibliographic record
Abstract
This article aims to assess the effectiveness of e-Government and e-Governance service during the national lockdown in South Africa. The focus of this article is on e-Health, e-Education and e-Municipal Services delivery, as these are the most sought-after e-Services during the national lockdown caused by COVID-19 (coronavirus) pandemic in 2020. Education, health, and municipal services are some of the core functions that could not be paused during the lockdown due to their importance. The methodology used in this research is mainly qualitative. Unobtrusive research techniques based on documentary and theoretical analysis will be applied to assess the state and use of e-Government and e-Governance within the public sector during the national lockdown in South Africa. The findings of this article suggest that government failed to achieve its objective of building an inclusive Information and Communication Technologies (ICTs) infrastructure in South Africa. Even though steps have been taken by the government to provide free access to basic e-Services, network coverage, and ICT infrastructures, poverty and inequality remain the major challenges in rural areas. The findings of this research suggest that the South African government needs to build ICT infrastructures in rural areas and to provide citizens with training on how to utilise ICT infrastructures in order to reduce the gap between rural and urban areas.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".