Bibliographic record
Abstract
Perhaps the greatest challenge 21st century globalization i to overcome the technological divide (this includes the digital and non-digital technologies) that exists between developed and developing nations. The uneven global uptake of technology is one of most important gauges of global inequality in the world today. Despite substantive efforts to level the playing field by bringing new opportunities to developing nations, this challenge continues to plague out modern world. It touches every area of human activity in society that depends on technology and change. It is both important in the current context and in future technology development. This raises the question about how it is that technology diffuses at a global level and how should this diffusion be regulated and controlled. For instance, Jeffrey (2001) examines the economic characteristics of ICT’s to gauge their potential effects on the global economy. Jeffrey (2001) found that ICT’s “are associated with a number of powerful cumulative mechanisms causing some countries to grow rapidly and others to become increasingly marginalized from the global economy (p. 147). According to Jeffrey, South Africa is among the most marginalized of developing nations despite substantive investment in their ICT infrastructure from the international development initiatives. Why is this the case and what can be done to improve the situation?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".