Categorization of the : Examples of a Domain of Notions in the Lexical Field
Bibliographic record
Abstract
In this paper, we organize a micro-world of «objects» (Cf. seats), specified by different kinds of parameters which are categorised in terms of “agentive templates”. We introduce the notion of “network of meaning” to integrate these templates. | .I. This paper resumes previous studies, already set in lexicology in the intention to redefine the notion of “referent” in discourse; in others words, the notion of “objects” as pieces of the world (real or imaginary). In this way, it is the definition of the relationship between the “signified” (in Saussurian words: the principle of a linguistic differenciation) and the “referents” as a description of the objects by means of “semic features”. One of the most well-known linguistic studies is Pottier’s work on seats (1963). The linguist introduced the notion of a matrix of combinatorial features in order to describe different types of seats (like chair, stool, armchair,…) according to definitional criterion: with/without back, with/without arms, fixed/folding, one seat/several seats, etc,… these features are denotative criterions by which we are able to caracterize the “mental image” of theses entities and through which we understand what they are; afterwards, we can introduce them in different kinds of discourse scenarii. Similarly, this thematic of the seats appears again in G. Lakoff’s book (1987: 52) who finds once again in Rosch’s analysis of prototype the relevance of the notion of “opposite features”. .II. The analysis that we propose cannot be only a mode of inventory of the data (which will be introduced in the creation of thesaurus). It has to allow us to build a notion of “representation of knowledge” that we have of these referents; in others words, the setting up of a process which means their cognitive organization; as such, this kind of entity is able to have: a use , < a
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".