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
KABISA is a computer-based program for training in diagnostic problems in (sub-) tropical regions. It challenges the individual student with a randomly generated case, for which he should try to find the diagnosis, asking questions, performing a physical examination, and ordering tests. The built-in tutor follows the student’s input with complex logical algorithms and mathematical computations, gives comments and support, and accepts the final diagnosis if sufficient evidence has been built up. Several problems arose with the development. In the first place, the evolution in the teaching of clinical logic is always ahead of the program, so regular updating of the computer logic is necessary. Secondly, the choice of MS Access as computer language has provoked problems of stability, especially the installation of an MSAccess runtime. Thirdly, and most importantly, scholars want proof of the added value of computer programs over classical teaching. Moreover, the concept of a pedagogical “game” is often regarded as childish. Finally, the planning and financing of an “open-ended” pedagogical project is questioned by deciders, as is the case with all operational research.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it