Inside Theory-Aware and Standards-Compliant Authoring System
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
In their paper [14], Bourdeau and Mizoguchi foresaw a framework for ontology-based intelligent systems. Although it took longer years than their expectation, the ontology they have been developing is now released for evaluation with the help of the second author. Ontology building is a labor-intensive process and it is rarely perfect. Our enterprise is not an exception. The current ontology is still very preliminary because it has been completely reconstructed from the existing one with a few new ideas. So, we hope the readers be generous when they read the ontology. The ontology presented here is not a light-weight ontology but a heavyweight ontology. It is built based on philosophical consideration of all the concepts necessary for understanding learning, instruction and instructional design. Although it is full of axioms, the Hozo GUI which is based on a frame structure makes it easier to read it. However, the readers are expected to have basic knowledge of ontology and preferably be aware of the theory of role and of the Hozo way of role representation. Papers [6][7] would be helpful to grasp what we are doing with this ontology. The prototype system named SMARTIES is a totally ontology-aware system which fully utilizes the merits of ontology computationally as well as conceptually. It is so preliminary that it cannot be open to public, though you can get a rough idea of what it is from the papers.
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 | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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