Bibliographic record
Abstract
In this paper, we present a software tool allowing for knowledge interchange between people. With this tool, domain knowledge is represented as a number of knowledge networks, each of them consisting of several knowledge units and links. At its core, knowledge units are modeled as semi-structured entities to favor their understanding by the users. Moreover, the tool can convey various resources (e.g. multimedia files) which are necessary for the understanding of procedural knowledge. We argue afterwards that the tool can support the creation of new knowledge and can help to construct a new domain ontology. We then give some practical reasons which have led us to define an appropriate scope for the basic units involved in the interchange of knowledge. In the context of permanent knowledge evolution, we explain how structured knowledge units (instead of individual keywords) as used. Finally, we report on some experimentation using this tool carried out by our team as part of an educational project.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".