Bibliographic record
Abstract
Abstract The possibility of referential null subjects in Old English has been the subject of conflicting assertions. Hulk and van Kemenade (1995:245) stated that “the phenomenon of referential pro -drop does not exist in Old English,” but van Gelderen (2000:137) claimed that “Old English has pro-drop.” This paper presents a systematic quantitative investigation of referential null subjects in Old English, drawing on the York-Toronto-Helsinki Parsed Corpus of Old English Prose (YCOE; Taylor, Warner, Pintzuk, & Beths, 2003) and the York-Helsinki Parsed Corpus of Old English Poetry (YCOEP; Pintzuk & Plug, 2001). The results indicate substantial variation between texts. In those texts that systematically exhibit null subjects, these are much rarer in subordinate clauses, with first- and second-person null subjects also being rare. I argue that the theory of identification of null subjects by rich verbal agreement is not sufficient to explain the Old English phenomenon, and instead I develop an account based on Holmberg's (2010) analysis of partial null subject languages.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".