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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 it