Bibliographic record
Abstract
In reply to the claim that syntax is not taken into account in Linguistic Meaning Meets Linguistic Form, I show that local syntactic analysis has been implemented in the treatment of aspectual verbs and verbs of positive and negative recall, where the syntactic function of the -ing form as direct object of the main verb is put into relation with the main verb’s meaning as the basis for the inferences drawn concerning the temporal relation between the main verb’s event and that expressed by the complement. I argue that I have also developed new tools of syntactic analysis for the to-infinitive, demonstrating that it is not the direct object of the main verb, but rather a goal- or result-specifier, and showing how this accounts for the fact that its event is always understood to be somehow subsequent to that of the main verb. Regarding the applicability of formal semantics to natural language, I argue that the absolute priority accorded to the truth-functional dimension of language by this type of semantics leads to the artificial separation of use-conditions from truth-conditions, with the former being treated as an additional interpretational function added on to the truth-functional one. Contra the autonomous syntax claim that our desire to express meaning is to a great extent independent of the means we use to express those meanings, it is argued that how we perceive the world in our experience is influenced by our system of linguistic representation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads agree on what is shown here.
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".