Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Digitalization is a core issue in training. This article introduces the main forms of digitalization and animation of digital devices by comparing their conditions of use. The combination of face-to-face and distance learning courses beats distance learning alone in terms of efficiency. As a demonstration, we propose a classification of digital devices according to four criteria: the level of interactivity; efficiency; the breadth of skills developed; and the ability of learners to use the knowledge acquired in a practical setting. This typology makes it possible to argue that, despite the diversity of technological tools, blended learning or game-based learning are favored because they enable experimentation and collaborative role-playing. Nonetheless, the effectiveness of the digitalized tools depends greatly on the respect of some fundamental principles that must be integrated into their design and in particular into the animation of the training. Generally speaking, a classic or online course is effective as long as it is tailored to the needs of its audience and focuses on a humanized training combining quality animation, interactivity, and a reflexive approach.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it