No Nouns, No Verbs: Psycholinguistic Arguments in Favor of Lexical Underspecification
Bibliographic record
Abstract
It is often assumed that the primitive units of grammar are words that aremarked for grammatical category (e.g., DiSciullo, A.M., Williams, E., 1987.On the Definition of Word: MIT Press, Cambridge, MA). Based on a review ofresearch in linguistics, neurolinguistics, and developmental psychology, weargue that dividing the lexicon into categories such as noun and verb offersno descriptive edge, and adds unnecessary complexity to both the theory ofgrammar and language acquisition. Specifically, we argue that a theorywithout lexical categories provides a better account of creative languageuse and category-specific neurological deficits, while also offering anatural solution to the bootstrapping problem in language acquisition(Pinker, S., 1982. A theory of the acquisition of lexico-interpretivegrammars. In: Bresnan, J. (Ed.), The Mental Representation of GrammaticalRelations. MIT Press, Cambridge, MA, pp. 655– 726).
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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 | 0.000 | 0.000 |
| 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) | 0.004 | 0.006 |
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".