From Hypotheses and Working Scenarios to Arguments Aimed at Alternating Traditional Training with Computer-Assisted Training
Bibliographic record
Abstract
Nowadays, the era that we live in can be described as the Information Age, sometimes even as the Knowledge Age. No matter what area of science and technology we look at, it is obvious that we are dealing with an ‘information overflow’ without precedent in the history of mankind. In this context, Educational Sciences are no exception, and recent advances in this field, considering Computer-Aided Training, would have been unthinkable, unmanageable, and unattainable without the support offered by modern information and communication technologies, in the sense of Learning Management Systems. In its turn, Computer-Aided Training via sustainability educational index is integrated into Knowledge Society, where it plays an important role, for educational data-dependent actors, in decision-making, problem-solving, analyzing trends, understanding their customers, and doing research, being closely linked with pedagogical requirements in decades. Through this paper we aim to show that, at the moment, it is appropriate not to rely only on classical training, or on the contrary to deviate too much from it, embracing only computer-assisted training. It is appropriate to be flexible and to choose to carry out the pedagogical act using both variants equally, by alternating them in a modular aspect.
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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".