The Information Revolution and the Digital Divide: a Review of Literature’, TechKnowLogia 2(1
Bibliographic record
Abstract
provide a common space where information could be shared without barriers. The expansion of the Web may have surprised even its creator. In less than ten years, the online population has grown to 180 million individuals across all continents, while an estimated 250,000 sites are added to the Web each month (www.net-surfin.com/page4.htm). Rapid expansion is not unique to the Web. Computers, a strange word some fifty years ago, are now common household items and integral parts of educational systems in many countries. At the end of 1998, more than 40 percent of the households in the United States owned computers and one-fourth had Internet access (NTIA, 1999). In October 1999, 90 percent of all Canadian schools were online; four out of ten students had used e-mail during the previous school year; and 30 percent had designed their own web sites
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".