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Record W6911378741 · doi:10.5281/zenodo.10620279

MoEML Mayoral Shows

2022· article· en· W6911378741 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Art and Culture Studies
Canadian institutionsUniversity of VictoriaMedicine Hat College
Fundersnot available
KeywordsProcessionPlan (archaeology)FolioEvent (particle physics)Publication

Abstract

fetched live from OpenAlex

This anthology is one of the outputs of a grant-funded project called Walking Texts in Early Modern London. The project has a dual interest in the multiple editions of John Stow’s Survey of London, which pageant writer Anthony Munday updated in 1618. Pageant book collector Humphrey Dyson worked with Munday to deliver a fourth, much-expanded folio edition in 1633. The editions of the 1598 and 1633 Surveys published on the MoEML site and the editions of the mayoral pageant books published here allow us to identify connections across this canon of walking texts—texts that transcribe a route on the City either literally in the case of the mayoral procession or imaginatively in the case of Stow’s ward-by-ward walk through London. Via MoEML’s GIS technologies, we also plan to publish geospatial editions of the event of the show later in the project. These editions will aggregate pageants diachronically by place of performance.This archive contains the complete static site 1.0 edition of the MoEML Mayoral Shows, published in December 2022.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.237
Threshold uncertainty score0.792

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2370.041

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 head0.054
GPT teacher head0.218
Teacher spread0.164 · 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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