Bibliographic record
Abstract
production, or other educational system.The essence of technology and therefore educational technology is knowledge about relationships (e.g., if we perform action X, there is a probability, P, that outcome Y will occur).But it seldom is clear which action X is most likely to produce the intended result Y. Systems research often may help us clarify our decision-making. LIFELONG LEARNING IN AN AGE OF TECHNOLOGYThis paper is animated by a concern for education, not simply for classroom instruction.More than half the world's children are not in school and the global need for .education has been rising.This forces us to refurbish our ideas about how to implement educational aspirations, as well as to augment existing educational manpower.Perhaps more important is that school costs (in both affluent and penurious nations) rise even more rapidly than enrolments or national incomes.We face an inevitable conclusion, "Les fails s'imposent done avec une evidence irrefutable: aucune pays au monde n'a les moyens P. David Mitchell is Professor and sometime Director of the Graduate Programme in Educational Technology at Concordia University, Montreal, Quebec.His research and teaching interests include systems analysis, computer assisted learning, artificial intelligence and computer-video hybrids (computer-based interactive video).
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.812 | 0.798 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".