Structuring the Lexicon of Old English with Syntactic Principles: The Role of Deverbal Nominalisations with Aspectual and Control Verbs
Bibliographic record
Abstract
This chapter is about the relations between the derived constructions and the lexicon of Old English. More specifically, its aim is to contribute to the classification of the verbal lexicon by considering the morphological relatedness between derived nouns and their verbal bases of derivation on the one hand, and the derived constructions that revolve around deverbal nominals, on the other. The theoretical basis of the study is provided by Role and Reference Grammar (Van Valin and LaPolla 1997; Van Valin 2005). The chapter has scope over aspectual verbs and control verbs. The data have been retrieved from the Dictionary of Old English Corpus and The York-Toronto-Helsinki Parsed Corpus of Old English Prose. The main conclusion is that the syntactic configurations of Old English, including derived constructions with deverbal nominalisations, constitute a principled basis on which the verbal lexicon can be structured. Conclusions are also drawn regarding the evolution of the English gerund and the acquisition of semantic and syntactic properties by deverbal nominalisations.
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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 | 0.000 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".