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Record W4220683734 · doi:10.1093/library/22.3.3

Two Tales of Piracy

2022· article· en· W4220683734 on OpenAlexaff
Peter W. M. Blayney

Bibliographic record

VenueThe Library · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicScottish History and National Identity
Canadian institutionsMuscular Dystrophy Canada
Fundersnot available
KeywordsReignProject commissioningGrammarLawHistoryPublishingPolitical sciencePhilosophyLinguistics

Abstract

fetched live from OpenAlex

Abstract While book-piracy is often thought of as an Elizabethan problem, the organized infringement of lucrative copyrights in fact began a few decades earlier, in the reign of Mary Tudor. In this paper the known details are presented of what are probably the two best documented cases from that reign. The first was the repeated commissioning and importation of editions of Lily’s Grammar from Geneva (some of them falsely dated). The second was the reprinting of a collection of sermons whose authorized printer had secured a patent to protect it. In the first case the patentee’s attempts to obtain satisfaction in Chancery were unsuccessful; in the second the author’s interests fared rather better in Star Chamber, and may thus have established a precedent for the prosecution of Elizabethan pirates.

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)0.012
metaresearch head score (Gemma)0.029
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: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0280.069
Scholarly communication0.0160.018
Open science0.0020.015
Research integrity0.0110.013
Insufficient payload (model declined to judge)0.0170.003

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 head0.027
GPT teacher head0.205
Teacher spread0.179 · 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
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
Published2022
Admission routes1
Has abstractyes

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