New Faces of Digital Divide and How to Bridge It
Bibliographic record
Abstract
The chapter aims to explore, through the lens of digital divide, the challenges to alleviating socio-economic and intellectual limitations for prosperity of each individual. Cutting-edge research is reviewed to discuss in what way new technologies and access to them really help to develop citizens who are able to contribute in creative and democratic ways to society. While much effort has been done in the past decade to bridge the digital divide by resolving access issues and usage issues, the recent studies seem to indicate that the gap at all levels, nation-wide, community-wide, special groups-wide still exists and even deepens, especially regarding digital inclusion and meeting needs of at-risk population. More systematic research and innovative practical solutions are needed to address all the aspects of digital divide: physical, financial cognitive, content, and political access; also, we have to consider the technological and social resonances of digital technologies in terms of digital literacy and development of critical thinking.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".