Classification and Ambiguity: The Role of Definition in a Conceptual System
Bibliographic record
Abstract
With the advent of the semantic web, the problem of ambiguity is becoming more and more urgent. Semantic analysis is necessary for explaining and resolving some sorts of ambiguity by inquiring into the relation between possibilities of predication and definition of a concept in order to solve problems of interpretation of natural language discourse. Computing is now confronting such problems of linguistic analysis (Diggelen et al. 2004), and it is worth inquiring into the development of linguistic studies that can be useful for developing the theoretical background of ontologies. Our proposal is to develop a workable solution that passes between the horns of the dilemma posed by the traditional metaphysical approach versus the modern relativistic account. We interpret the ancient notion of essential definition in a pragmatic perspective, and show how the dialectical definition by genus and difference corresponds to the semantic analysis of the definiendum.
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.
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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.008 | 0.077 |
| Scholarly communication | 0.018 | 0.054 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".