Bibliographic record
Abstract
Rada sees cultural transmission as the aim of the university and sees teaching as the principal means of achieving it.The virtual university, with its digital nervous system to support thinking and collaboration, extends existing social mechanisms for transmission of accumulated knowledge.The understanding to which Understanding Virtual Universities contributes is based on: psychological principles of teaching and learning related to computer-mediated learning; analysis of recent and current use of information and communications technology in higher education; the author's broad experience as scholar, teacher, and administrator; the history of universities and technology; integrated-systems thinking; means-end analysis and quality control; cost-benefit analysis; knowledge management; involvement of business and industry in higher education; and analysis of new market opportunities for universities.The book emphasizes an approach to using information and communication technology that is tightly integrated across learning, teaching, and administration, in a rapidly changing social context.Roy Rada is an active scholar in the fields of health care information systems, virtual educational organizations, and workflow management who holds degrees in psychology (B.A.), medicine (M.D.), and Computer Science (Ph.D.).He has 20 years experience developing and using online collaborative learning systems.In addition to teaching
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".