This sentence sucks to analyse: Are <i>suck, bite, blow</i>, and <i>work tough</i>-predicates?
Bibliographic record
Abstract
Abstract This paper investigates tough -predicates and whether four verbs ( suck, bite, blow , and work ) can function as this type of predicate. The theoretical analysis uses two syntactic and two semantic properties of prototypical tough -predicates to determine the status of the tough -verb candidates. Syntactically, tough -predicates select a to-infinitival complement and require a referential dependency between the matrix subject and the object gap in the complement clause. Semantically, the matrix subject must possess an inherent or permanent property and tough -predicates assign an “experiencer” role. From these four diagnostic properties, the analysis concludes that suck, bite , and blow are indeed tough -verbs, while the conclusions concerning work are less definitive. To complement the conclusions of the theoretical analysis, native speaker judgements were collected from 22 Canadian English speakers. The results show that for a majority of the consultants, suck, bite , and blow can function as tough -predicates. The behaviour of these verbs suggests that suck, bite , and blow (and possibly work ) should be added to the small list of known tough -verbs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".