Bibliographic record
Abstract
This article argues for a dependency between structural Case and phasal domains and against Case values as intrinsic properties of (C)-T and (v*)-V. Rather, Nominative or Accusative values are derived compositionally from properties of the entire Probing domain: (i) Nom occurs whenever the Probing domain is specified as [uD, uf/p], while (ii) Acc is assigned if the Probing domain is specified as [uD]. The presence of a [uCase] feature is assumed on all DP arguments, whether null or overt. However, after Case valuation, DPs with inherent intensions and extensions will be lexicalized but variables, such as PRO, will not. The analysis focuses on DP subjects (both lexical and PRO) in non-finite CPs, and relies on availability of null expletive pro as a UG primitive. It assumes Chomsky’s Feature Inheritance Model (Chomsky 2007, 2008, Richards 2007), default Case as in Schütze (1997, 2001), as well as Distributed Morphology (Halle and Marantz 1993, Embick 2007). It aligns with views where the Case Filter, while syntactically relevant (Legate 2008), is a PF constraint (Lasnik 2008, Sigurðsson 2008).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".