Bibliographic record
Abstract
This paper discusses a new model of digital twins suitable not only for industries but for personalized education and life-long learning. In the past, professional education lasted for a lifetime through all the industrial revolutions, including the third. During those revolutions, not only the pace of knowledge doubling has accelerated from a lifetime to months, but the half-life of pertinent knowledge has also shortened. Are we capable of adjusting to that pace? How can we learn all that is needed in the old Prussian model of education based on one-program-fits-all? Since professionals have to migrate through multiple job environments, we should revamp the educational system at the core. The new system must be personalized to match the diversity of individual abilities and styles of learning. The new system must also be based not only on the traditional body of knowledge (BoK), but also on the body of experience (BoX) and body of memetics (BoM). The new personalized system of learning must be sufficiently agile and interactive so that it would become evolving in its symbiosis with humans. For that to happen, we should consider coexisting with memetic symbiotic autonomous cognitive systems, specifically involving digital twins. This paper addresses some aspects of this view.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".