Towards Human Security through Personalized Trans-disciplinary Evolving Symbiotic Education Based on Cognitive Digital Twins
Bibliographic record
Abstract
Education has been evolving through a complicated roadmap to serve varying objectives from the understanding of the world we live in through training of servers of production lines, after the first industrial revolution (IR1) to other commercial targets throughout the next three industrial revolutions. With the current scientific and technological progress, human security and sustainability were expected to take care of themselves and evolve naturally. Not only they have not evolved, but the threats triggered by the developments in artificial intelligence (AI) tools alone are becoming existential. Our insensitivity and neglect of the ecosystem must now be transformed very urgently. Education is central to that transformation. The traditional uni-disciplinary one-size-fits-all approach to education appeared to have been sufficient through the first three industrial revolutions as it served the learners for a lifetime. However, it must evolve now into a multi- and trans-disciplinary collaborative model to cope with the exponential growth of knowledge and the complexities of our ecosystem stressed to the limit. One of the possible enablers of the transformation is the concept of cognitive digital twins (CDT) which is maturing due to the developments in high-performance computing and artificial intelligence. This paper addresses some aspects of this view.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".