Assessing the Effectiveness of e-Government and e-Governance in South Africa: During National Lockdown 2020
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it