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Record W1600975550 · doi:10.3138/flor.26.001

Foreword: A Paean for the <i>Dictionary of Old English</i>

2009· article· en· W1600975550 on OpenAlexvenueaboutno aff
E. G. Stanley

Bibliographic record

VenueFlorilegium · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicLexicography and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLexicographyGermanHistoryClassicsLiteratureLinguisticsArtPhilosophyArchaeology

Abstract

fetched live from OpenAlex

In retrospect the foundation of the Dictionary of Old English reads like the New World coming to the aid of the Old. Its founder, Angus Cameron, had the vision and the hope needed. His dissertation on a difficult Old English word had shown to him the insufficiency of Old English lexicography, no better really in the late 1960s than it had been a hundred years before. Neil Ker’s Catalogue of Manuscripts Containing Anglo-Saxon had been published in 1957, and Angus for his thesis had rearranged its contents by turning it into a classified catalogue of texts for him to use as he hunted through the texts for his word in its many divergent senses. The standard dictionary at that time was effectively a work of the 1830s and earlier, supplemented by a good Mancunian scholar at the turn of the century. The work was mainly at second hand, relying on two good German lexicographers and on glossaries and translations of Anglo-Saxonists some good, others less so. Angus’s vision was that the basis was the textual evidence of the manuscripts and that a new age of technology had dawned, available in Canada, rich then, and led and encouraged by John Leyerle, of the States (resident as a professor in Toronto), a conference was organized to give substance to a hope. The assembled Anglo-Saxonists were united in spirit — rye, in my recollection — and it was determined that Toronto, with space provided in the Robarts Library by the University of Toronto, would be an excellent place for a new dictionary based on new technology, photocopies of manuscripts, so that all texts could be checked rather than merely used at second hand, and on the new electronic invention, the computer, at that early stage of its rapid development, especially good for concordances. A Canadian, Elaine Quanz, who had worked with Angus, was given the task of typing out all the texts of Old English, several thousands of them, for concording by the computer. If you wish to build high you need a firm foundation, and Angus saw to it that the foundation of the new dictionary was firm.

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)0.001
metaresearch head score (Gemma)0.005
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: Editorial · Consensus signal: none
Teacher disagreement score0.133
Threshold uncertainty score0.445

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0030.001
Scholarly communication0.0070.007
Open science0.0010.002
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.1330.160

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 head0.017
GPT teacher head0.212
Teacher spread0.195 · 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
GenreEditorial

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

Citations0
Published2009
Admission routes2
Has abstractyes

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