Investigating the Impact of ICT Investments on Human Development
Bibliographic record
Abstract
Abstract In the last two decades, the worldwide information and communication technology (ICT) market has been growing at a rapid rate. This has led to a global net increase in ICT investments and usage. International organizations, ICT vendors and policy makers have been investigating whether such large investments are worthwhile. The results regarding this issue are inconclusive, for this research area is fraught with complexity, and existing empirical work is limited. This study investigates the impact of ICT investments on human development. Of particular interest are the relationships between different dimensions of ICT investment and the components of human development. ICT investments can be thought of as having four dimensions – hardware, software, internal spending and telecommunication investment, while typical human development indicators are standard of living (GDP per capita), education (literacy and school enrolments) and health (life expectancy). If these variables are not modelled correctly, their effect on each other can be either under‐ or overestimated. In this article, the line of enquiry from a study by Kim et al. (2008) is extended to investigate the relationship between the four aspects of ICT investments and the three key components of human development. The empirical analysis shows that the four dimensions of ICT investment have an impact in various ways on the components of human development, and that these impacts are different, in high income, mid income and low income countries. Based on these findings, this study suggests theoretical propositions to explain the impact of ICT investments on human development.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".