Bibliographic record
Abstract
The five papers in this volume arose from three sessions the editors organized at the nineteenth Triennial Conference of the International Association of University Professors of English at the University of British Columbia, Vancouver, in August 2004.All the essays treat texts either from the Old English period or from the transitional twelfth century, and each explores, from differing perspectives, how today's readers make sense of, or construct meanings from, early English documents.The first two essays specifically focus on the research tools of the Dictionary of Old English as strategic aids in the discovery of meaning, and as instruments for interrogating legal, religious, social, cultural, and linguistic issues of the period.The other three, while also availing themselves of the Dictionary of Old English, focus on how scholarly editing tries to make sense of the complex ways medieval documents themselves attempted to make sense -through evolving translations, variant versions, selective adaptations, explanatory glosses, expansive commentaries -of the "same" texts over this long and linguistically diverse span of time.In"F-words in Beowulf," Roberta Frank opens with some lighthearted statistics, but deftly proceeds to uncover some unexpected meanings through an analysis of thirteen salient F-words, filtered through their entries in the Dictionary of Old English.Frank contextualizes these words in time as well as in meaning by exemplary quotations from other texts or from other related words.She astutely observes that "the DOE f-fascicule reveals just how rare some of the ordinary sounding words in the poem are."And her convincing demonstrations suggest how simple juxtapositions of these rare words in Beowulf to their uses in other poems, legal records, Old Norse cognates, and especially religious texts, can lead to new understanding or to the recovery of traditional
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.342 | 0.146 |
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".