Bibliographic record
Abstract
The epic poem Beowulf is one of the best known and most widely translated of the extant Old English texts. Nevertheless, despite more than a century of scholarly debate, there is no absolute agreement on when the poem was written. Although a date of about 1000 is generally accepted for the one surviving manuscript, various types of evidence – archaeological, codicological, cultural, historical, linguistic, metrical, onomastic, paleographical, philological, political, semantic, sociological, theological – identify the poem's date of composition at various points between the seventh and the early eleventh centuries. Much of the linguistic evidence has been phonological (i.e., metrical) and morphological in nature rather than syntactic. Although some grammatical criteria have been developed for dating, they are not developed within current generative syntactic frameworks but rather based upon word order and the selection of lexical items that do not necessarily reflect syntactic distinctions. This is not surprising, for at least four reasons. First, much of the work on grammatical dating criteria was carried out before linguists started to investigate the formal syntax of Old English in generative frameworks. Second, most syntacticians do not attempt to analyze the language of poetry, since the syntax may be influenced by poetic constraints that do not affect prose texts and thus add an extra layer of difficulty to the investigation. Third, it is only within the last three decades that we have accumulated sufficient knowledge about the syntax of Old English prose and the quantitative patterns of syntactic variation and change during the Old English period to enable us to start to analyze the poetry. And finally, scholars writing before the release of two annotated corpora – the York-Helsinki Parsed Corpus of Old English Poetry (YCOEP) in 2002 and the York-Toronto-Helsinki Parsed Corpus of Old English Prose (YCOE) in 2003 – were handicapped by not having the benefit of being able to collect and quantitatively analyze Old English data quickly and easily. Fulk has clearly expressed his views on the nature of evidence and argumentation in linguistics: most science is built on hypothesis formation, and hypotheses can never be proved in the mathematical sense, but can only be rendered extremely probable. (Fulk 1992: 8)
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".