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Record W2317697298 · doi:10.1093/res/hgw036

LEONARD NEIDORF, ed.<i>The Dating of</i>Beowulf:<i>A Reassessment</i>.

2016· article· en· W2317697298 on OpenAlexaboutno aff
Eric Weiskott

Bibliographic record

VenueThe Review of English Studies · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicMedieval Literature and History
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipPoetryHistoryFaithClassicsDivinationSkepticismLiteraturePhilosophyArtTheologyLawPolitical science

Abstract

fetched live from OpenAlex

This collection, published under the same title as the 1981 University of Toronto Press volume that it hopes to supersede, grew out of a conference at Harvard University. Thirteen contributors reconsider the dating of Beowulf , and each of them concludes or accepts that Beowulf was composed in the eighth century. As the singular noun of the subtitle suggests, this Dating of ‘Beowulf’ is less a report from the field than a concerted provocation. Leonard Neidorf’s polemical introduction traces a selective history of the debate over the dating of the poem. Neidorf sorts Beowulf scholarship into two bins: studies pursuing ‘affinities between the poem and a given period of Anglo-Saxon history’ (p. 3) and those relying on ‘hard evidence’ (p. 4) with ‘probabilistic force’ (p. 16). This volume will enable the dating of Beowulf ‘to be based in reasoning rather than divination’ (p. 17), the somewhat brash implication being that most prior research has been on the side of faith, not science.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0030.004
Scholarly communication0.0050.008
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.005

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.

Opus teacher head0.040
GPT teacher head0.289
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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