Information and Communications Technology Development and the Digital Divide: A Global and Regional Assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it