Bibliographic record
Abstract
The Internet has become increasingly an important source of information for pupils in learning, entertainment, sharing and exchanging experiences with other peers, meeting with adults and learning about other cultures. The Internet is a major force in the lives of school children and is no doubt having a tremendous influence on their reading habits and information seeking behaviour. With increased Internet connectivity however, concerns are being raised about the widening imbalances of access to ICTs between north information haves and the information have nots countries. This imbalance known as the digital divide has implications in terms of equity access to quality education in an electronic age. In Africa there are limited programmes that address in particular how schools can be equipped to benefit from the digital age and at the same time be used as an important instrument to bridge the digital divide. The concept digital divide was coined to describe the imbalance in access to information and communication technologies by different communities of people between different countries of the world. This phenomenon is today known to exist within individual countries, cities and even communities. Today schools are being seen as one of the most salient infrastructure that can be used to bridge the digital divide in our midst. This paper looks at developments in Africa aimed at bridging the digital divide through schools.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".