Bibliographic record
Abstract
The paper posits that there are at least five key interlinked representation levels which cumulatively inform the development of any Semantic Knowledge Management (SKM) model, namely, perception, language, ontology, taxonomy and description. To that end, drawing from concepts in knowledge representation, the paper illustrates how representation entanglement impacts the above representation layers culminating in an entangled final SKM model in an SKM exercise. Finally, the paper proposed a representation disentanglement approach to disentangle the aforementioned entanglement leading to the generation of a disentangled SKM model. Gestion des connaissances sémantiques: vers une représentation et une perspective novatrice de la connaissance RésuméCet article part du principe qu’il y a au moins cinq niveaux de représentation interconnectés qui vont, cumulativement, détailler le développement de tous modèles de gestion des connaissances sémantiques (SKM), à savoir, la perception, le langage, l’ontologie, la taxonomie et la description. A cette fin, en s’appuyant sur des concepts de représentation de la connaissance, l’article illustre comment l’enchevêtrement de la représentation affecte les couches de représentation ci-dessus, aboutissant à un modèle SKM final emmêlé dans un exercice SKM. Finalement, l’article propose une approche de démêlage par représentation pour dégager les enchevêtrements susmentionnés, conduisant à la génération d’un modèle SKM clair. Mots-clésgestion de la connaissance; représentation
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.012 |
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".