Bibliographic record
Abstract
This study proposes an approach to linguistic semantics under which the values of the substantive distinctions in grammar (adjective, proper noun, common noun, ec.) are not lexically specified on terms, but rather follow from the application of the rules that combine terms into sentences.At the level of the term, all substantive elements have the same value, that of "atom" or "nondecomposable unit", and their denotations (the notions they are associated to in the conceptual domain) only serve to distinguish them from one another.The categorical distinctions then emerge from how the syntactic rules manipulate a term's basic atomic vaue.Whether the result is felicitous depends on what the term denotes in the conceptual domain.With this approach, we show how we can account for the different categorial values of the expression red (adjective, as in Mary's favourite car is red, and common noun, as in Mary's favourite colour is red) with a unified lexical description.The semantic value of substantive elements in grammar is thus derived, since it emerges from the application of combinatorial rules.The lexical vocabulary, which in principle cannot be derived, is thus optimal, since each form can be associated with one conceptual meaning at the lexical level.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".