Variation in Old English revisited
Bibliographic record
Abstract
Corpus linguistics can be divided into two major avenues of research — corpus-based: Searching a corpus based on preexisting hypotheses or intuitions, and corpus-driven: An unbiased search of a corpus independent of any framing hypothesis or intuition. Corpus-driven methods have been touted to be more proficient in identifying previously undocumented patterns. This article revisits the variation observed in Old English (OE) by first discussing some of the existing corpus-based studies and their findings. Next, a corpus-driven methodology of exploration based on generating and searching for all possible permutations of selected syntactic labels (S, V, O, p and Aux.), i.e., all possible word order patterns is presented. Finally, after applying the corpus-driven methodology to the York-Toronto-Helsinki Corpus of Old English (YCOE) and outlining some broad assumptions that are valid cross-linguistically, the word order patterns attested in YCOE are syntactically analyzed — of note is the in-depth analysis of embedded adverbial adjunct clauses with respect to CP-recursion. This study documents and presents analyses of an extensive list of word order patterns in OE and categorically verifies certain theories of OE syntax, and challenges others. To the best of our knowledge, the study presented in this article is the first corpus-driven investigation of the variation observed in OE. More generally, this study lays a foundation for future corpus-driven and corpus-based research on Old English syntax.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".