Bibliographic record
Abstract
The educational system components consist of sources of knowledge, a communication channel between sources and students, and student processing of the knowledge with or without human intervention. The Internet has introduced a low cost world-wide communication channel on which educators are working to take full advantage. But the Internet is also proving to be a vast source of relatively free knowledge. The consequence is a growing awareness that broad application of a learner centered model of education is rapidly becoming feasible. This educational model involves a major innovation and the United States elementary and secondary educational systems are highly resistant to innovation. Therefore, it is appropriate to review how other parts of the world are taking advantage of this opportunity. The Journal has been attracting numerous papers from the international community and the first article in this issue is from the Korea National University of Education and describes four international efforts to develop learner centered educational systems using available information and communications technology (ICT). Written by Anthony Pich and Bokyeong Kim, the article is entitled “Principles of ICT in Education and Implementation Strategies in Singapore, the Province of Alberta in Canada, the United Kingdom and the Republic of Korea.” The progress reported in these efforts illustrates the positive side of having educational systems strongly controlled by a central government agency. The discussion of the Korean effort, however, also shows the difficulties in implementing this educational innovation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.084 | 0.055 |
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".