Using Information Technology to Create Global Classrooms: Benefits and Ethical Dilemmas
Bibliographic record
Abstract
The global digital divide represents one of the most significant examples of international inequality. In North America and Western Europe, nearly 70% of citizens use the Internet on a regular basis, whereas in Africa less than 4% do so. Such inequality impacts business and trade, online education and libraries, telemedicine and health resources, and political information and e-government. In response, a group of educators and community leaders in South Africa and the United States have used various information technologies to create a ?global classroom? that connects people in the two countries. University students, high school students, and other citizens communicate via Internet exchanges, video conferencing, and digital photo essays. The project has produced a number of tangible benefits and it has developed a model for reducing inequality in global education, at least for those institutions with the technological resources to participate. We also present several recommendations for how to expand the initiative and thereby increase the number of people who can benefit from it.
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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 | 0.062 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".