Control in Free Adjuncts in English and French: a Corpus-Based Semantico-Pragmatic Account
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: Bill ordered Fred to leave immediately. Fred’s order from Bill to leave immediately. The order from Bill to Fred to leave immediately. Fred received Bill’s order to leave immediately. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".