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Record W2618569315 · doi:10.16995/dm.59

Alliteration and character focus in the York Plays

2015· article· en· W2618569315 on OpenAlexaffvenue
Richard Khoury, Douglas W. Hayes

Bibliographic record

VenueDigital Medievalist · 2015
Typearticle
Languageen
FieldComputer Science
TopicAuthorship Attribution and Profiling
Canadian institutionsLakehead University
Fundersnot available
KeywordsAlliterationFocus (optics)Character (mathematics)Context (archaeology)Reading (process)LinguisticsNatural language processingComputer scienceHistoryMathematicsRhymePhilosophyPoetry

Abstract

fetched live from OpenAlex

This paper presents the first complete statistical study of alliteration in the York Cycle of Mystery Plays. To this end, an algorithm is designed to render the phonetic reading of the words of the play and to measure alliteration in the speeches of individual characters. Next, the alliteration statistics of the characters are studied in the entire Cycle, and in each individual play, in order to gain new insight on the possible significance of that linguistic feature in the Plays. Our results indicate that alliteration may have been used as a tool to focus the attention of the audience on one or two major characters in each individual play. Taken in the context of the entire Cycle, there is also a hint of repeating patterns in the manner that alliteration is used within the play.York Cycle, Alliteration, Medieval Theatre, Digital Humanities, Natural Language Processing, Stylometry, Computational Text Analysis

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.066
GPT teacher head0.277
Teacher spread0.211 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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
Published2015
Admission routes2
Has abstractyes

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