ICT Infrastructure Expansion in Sub‐Saharan Africa: An Analysis of Six West African Countries from 1995 to 2002
Bibliographic record
Abstract
Abstract A decade has passed since many African countries started consistently investing in the new Information and Communication Technology (ICT). Currently, there is need for more research on the impact of these investments on the expansion of productive capacity necessary for economic and social developments in this region on the world. Presently, the African Telecommunication Union (ATU) is advocating higher levels of investment in ICT in African countries, regional integration and new policies for the ICT sector. High‐tech parks are being constructed for the development of the technology and to attract and encourage business initiatives in the sector. However, UNDP agencies for information technology and social development have not yet been able to state firmly whether the adoption of ICT has had a significant impact on less developed countries in general and African countries in particular. In this paper, I demonstrate that the investments in the ICT sector have resulted in technical progress. This study uses a DEA approach and some novel analysis to examine the impacts of investments in the ICT sectors of six West African countries during the period of 1995 and 2002.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".