Bibliographic record
Abstract
Information and communications technologies (ICTs) have become key tools and had a revolutionary impact of how we see the world and how we live (Dabesaki, 2005). They have the potential to be a major driving force behind the economic growth of any nation because of their potentially strong restructuring impact on existing economic activities and the ability to affect economic activities in a variety of ways. These include improving the quality of existing services, creating new services, raising labor and productivity, increasing capital intensity, enhancing economics of scale, and creating new economic structures. ICTs are also paving the way for greater ease of movement of technical and financial services, and are instrumental to development during the rapid globalization process. From the information technology revolution, a new kind of economy emerges. This is the information-based economy in which information along with capital and labor is a critical resource for creation of income and wealth for the enhancement of competitiveness. ICTs have also left their mark on the political and social dimensions of development, specifically by enhancing participation in decision-making processes at the corporate, local, and national levels. It is an established fact that a few developing countries like China, India, and Brazil are successfully taking advantage of the opportunities information and communications technologies offer and have made significant improvement in their economic, and many more developing countries (including Nigeria) are beginning to derive some of the potential benefits. For most of the developing world, however, information and communications technologies remain just a promise, and it seems a distant one at that. There is little evidence from past experience of national and international development policies, strategies, and programs to suggest that much will change for large segments of the world’s poorest people. Nigeria, like most developing countries, is an “information- poor” country where the deployment and application of ICTs is still in its infancy. This article, which is an updated version of an earlier one (Ajiferuke & Olatokun, 2005), presents the current status of ICT in Nigeria, particularly its applications in some sectors of the nation’s economy. It also identifies some inhibitions to the effective deployment and exploitation of ICT in Nigeria and concludes with a discussion of the policy issues, challenges, and prospects of ICT use in Nigeria.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".