Bibliographic record
Abstract
Eight years have passed since this book first appeared, and of course much has changed.In presenting this revised edition, I have attempted to correct as many literal errors as I have detected, and I am thankful to those reviewers who helped steer me in the right direction.I am especially grateful to David McDougall of the Dictionary of Old English for his detailed help in this regard, and to Michael Fox and Samantha Zacher for their assistance.Doubtless, errors still remain: the faults still remain mine.There has, of course, been much work done on Beowulf and the other texts mentioned here in the intervening period, and it may be helpful to signal just a few relevant works.The most important new tool available is undoubtedly the Electronic Beowulf * ed.Kevin S. Kiernan et al., 2 CDs (London, 2000), which has made the Beowulf-manuscript accessible to many.Other monographs that have focused on aspects of the texts and monsters mentioned here include Christine Rauer, Beowulf and the Dragon: Parallels and Analogues (Cambridge, 2000), and Magnus Fjalldal, The Long Arm of Coincidence: The Frustrated Connection between 'Beowulf and 'Grettis saga'
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.351 | 0.251 |
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".