4. Current challenges to the Lexicalist Hypothesis: An overview and a critique
Bibliographic record
Abstract
In this chapter, arguments against several variants of the modern syntax-based analyses of deverbal nominalizations are presented, and the classic lexicalist approach deriving from Chomsky’s 1970 Remarks on nominalization is defended. The modern approaches of Alexiadou (2001), Fu, Roeper and Borer (2001), Harley and Noyer (1998), which revive in various forms the sentential Generative Semantics analyses of event nominals, are each considered and rejected in turn. In such approaches, argument-structure nominals contain some amount of verbal structure as a proper subpart. Yet, all such nominals exhibit surface syntactic patterns that resemble exactly those of nonderived nominals. The absence of verb-phrase syntax within nominalizations is a fundamental generalization about such nominals, and is very problematic for analyses which propose such substructure.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".