The interplay of silent nouns and (reduced) relatives in Malay adjectival modification
Bibliographic record
Abstract
This paper considers parametric variation in the area of adnominal adjectival modification from the viewpoint of Malay. Cinque (2010) has shown that, in spite of the great deal of variation found in adjectival modification, it is possible to identify two main classes with clear-cut syntactic and semantic properties: direct and indirect modification. Working on a restricted subset of adjectival classes, namely intersective, subsective and evaluative adjectives, we put forward a general proposal aiming to characterize in a precise way the syntactic distinction between these two main types of adjectival modification. Our proposal crucially involves the presence of silent/overt nouns, cf. Kayne (2005), and a possessive relation in the case of direct modification, and (reduced) relatives for indirect modification. Under the set of proposals put forward in this paper, variation will mostly follow from (a) externalization, cf. Berwick and Chomsky (2011), Chomsky (2010), Richards (2008), Di Sciullo (2015), and (b) the set of silent nouns available, a “lexical parameter” of a quasi-inflectional nature, cf. Chomsky (2001).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".