Control in Free Adjuncts in English and French: a Corpus-Based Semantico-Pragmatic Account
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Three main sorts of approaches to control can be found in the linguistic literature: syntactic, semantic and pragmatic. The syntactic approach can be exemplified by Boeckx et al. (2010), who treat obligatory control as syntactic movement rather than binding, making PRO ‘simply a residue of movement — the product of the copy-and-deletion operations that relate two theta-positions’ (Hornstein 1999: 78). Thus in the derivation of John hopes to leave, John starts out in the subordinate VP [John leave] and raises to the sentential level, checking two theta-roles on its way and ending up with two cases, one corresponding to the ‘hoper’ and the other to the ‘leaver’ role. This purportedly explains the subject control reading (henceforth SC). In a purely conceptual approach such as that of Culicover and Jackendoff (2005), it is the semantic content of the matrix verb rather than syntactic movement which is the key factor. They argue that since control remains constant with a given lexical notion over a wide variety of constructions it cannot be a syntactic phenomenon — thus in (1a-d) below with the notion ‘order’, the NP Fred is understood to control leave in all cases even though its syntactic position varies considerably:
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it