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Record W4388058045 · doi:10.3406/scrip.2015.4323

Palaeographic preferences and manuscript contexts of a mid-eleventh-century English legal collection: London, British Library Cotton MS Nero A I (part A)

2015· article· en· W4388058045 on OpenAlexaboutno aff
Thomas Gobitt

Bibliographic record

VenueScriptorium · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicMedieval Literature and History
Canadian institutionsnot available
Fundersnot available
KeywordsEleventhQuarter (Canadian coin)HistoryClassicsArchaeology

Abstract

fetched live from OpenAlex

This article examines the codicological and palaeographical features of London, British Library Cotton MS Nero A I, fols 3-57 (part ‘ A’), a collection of Anglo-Saxon laws and related texts produced in the third quarter of the eleventh century. Part ‘ A’ itself may have been a composite, with one of the exemplars comprising a self-contained copy of the I-II Cnut law-code and the other being a collection showing interest in the role and duties of judges as well as in the law itself. Detailed study of the codicological features and palaeographic preferences of the contributing scribes allows some of the manuscript’s complex stratigraphy to be unravelled. The production and use of the manuscript demonstrate how scribes and readers continually re-invented both texts and books to suit their ends.

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)0.006
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.102
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0140.008
Scholarly communication0.0090.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.021
GPT teacher head0.189
Teacher spread0.168 · 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
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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