Can syntax swallow semantics? Or might it not be the other way around?
Bibliographic record
Abstract
Abstract In the Generative model of human language, the semantic component has the role of interpreting the syntax. This amounts to semantics being swallowed up by syntax and explains Chomsky’s reiterated statements that at bottom natural language has only syntax and pragmatics: “natural language has no semantics … it has syntax (symbol manipulation) and pragmatics (modes of use of language)” (Chomsky, Noam. 2013. Notes on denotation and denoting. In Ivano Caponigro & Carlo Conchetto (eds.), From grammar to meaning: the spontaneous logicality of language , 38–45. Cambridge: Cambridge University Press). It will be argued that this conclusion is predetermined by the view of language as a computationally perfect syntactic system. Language is thereby reduced to syntactic distribution and symbol manipulation, linguistic form separated from meaning, with syntax being investigated according to the goals of coherence and consistency and subsequently reconnected extrinsically to the world by means of pragmatics. It will be shown that a basic understanding of the nature of language, which is constituted by the fundamental correlation between linguistic signs and their meanings, reveals that one cannot isolate and study separately the physically observable side of language without tearing asunder the very object of one’s investigation. The causal conditioning of syntax by both semantics and pragmatics will be demonstrated via the examples of English causative verbs and the ‘degree adverb + proper name’ construction.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.046 |
| Scholarly communication | 0.010 | 0.028 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".