The digital divide: global and regional ICT leaders and followers
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 “digital divide” has sparked serious debates along the lines of economic disparity among world nations. Many in academics and policy circles believe that the digital gap could further widen the economic gap between developed versus developing nations. Among the components that are taken into consideration for measuring and analyzing the digital divide between countries, the information and communication technologies (ICTs) is the key component. This paper adds to the existing body of knowledge on the issue of regional and global digital divide by profiling 192 member countries of the United Nations based on their ICT indicators. Using clustering and statistical analysis, our results identify “leaders” and “followers” in ICT infrastructure and utilization at both regional and global settings. Mina Balliamoune-Lutz is the accepting Associate Editor for this article.
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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 | 0.000 |
| 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