Is There A Relationship Between ICT, Health, Education And Development? An Empirical Analysis of five West African Countries from 1997–2003
Bibliographic record
Abstract
Abstract For more than a decade, key international organizations such as the World Bank, International Monetary Fund, the UN and International Telecommunications Union (ITU) have argued that investment in information communication and telecommunication (ICT) infrastructure is a prerequisite for the development of poor countries. However, dissenting voices of the international development community argue that African governments should focus their attention on building schools, delivering basic health care, electricity and clean water rather than on the building of costly ICT infrastructure with their limited financial resources. In this paper, we present an analysis of the relationships among investments in ICT, Health Care and Education and the human development index on five West African nations. We use a Stepwise regression analysis to help unravel the complex relationships among these variables. Our results provide evidence that complementary investments in ICT, health and education can significantly increase development. Given that developing nations are making considerable investments in healthcare, education and ICT and that there are concerns over the type of investments they should make, our findings are a significant contribution to the literature.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".