MétaCan
Menu
Back to cohort
Record W4200160069 · doi:10.16995/dm.8065

Well-Behaved Variants Seldom Make the Apparatus: Stemmata and Apparatus in Digital Research

2021· article· en· W4200160069 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueDigital Medievalist · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsNexus (standard)Computer sciencePhylogenetic treeVariation (astronomy)XMLMaximum parsimonyData scienceBiologyWorld Wide Web

Abstract

fetched live from OpenAlex

This article describes computer-assisted methods for the analysis of textual variation within large textual traditions. It focuses on the conversion of the XML apparatus into NEXUS, a file type commonly used in bioinformatics. Phylogenetics methods are described with particular emphasis on maximum parsimony, the preferred approach for our research. The article provides details on the reasons for favouring maximum parsimony, as well as explaining our choice of settings for PAUP. It gives examples of how to use VBase, our variant database, to query the data and gain a better understanding of the phylogenetic trees. The relationship between the apparatus and the stemma explained. After demonstrating the vast number of decisions taken during the analysis, the article concludes that as much as computers facilitate our work and help us expand our understanding, the role of the editor continues to be fundamental in the making of editions.

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.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.034
GPT teacher head0.298
Teacher spread0.264 · 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