Information and Communications Technology Development and the Digital Divide: A Global and Regional Assessment
Bibliographic record
Abstract
The rapid development in information and communications technologies (ICTs) has created a wealth of opportunities for businesses and societies around the world. Yet, the disparity in the ICT adoption between developed and developing countries, often referred to as the Digital Divide, continues to widen. As a result, the digital divide has remained an issue of significant importance to policy-makers and scholars. In an effort to measure the magnitude of the digital divide and monitor how the disparity evolves over time, the United Nations commissioned the development of a comprehensive ICT Development Index (IDI) in 2009. The objective of this paper is to extend the methodology used in the IDI project and other scientific results presented in previous research to measure the digital divide. Using data mining techniques, we analyze ICT profiles from 154 countries to provide a rigorous quantitative assessment of the digital divide. In addition to analyzing the digital divide at the global level, we present our results at a regional level by identifying countries that are leaders and followers in their respective geographical area. Moreover, our analysis found that between 2002 and 2007, nine countries have made a significant progress in ICT adoption such that they have transitioned into a group previously consisting primarily of developed countries.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".