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Record W4200398421 · doi:10.16995/dm.8066

You’re Collating Just Fine and Other Lies You’ve Been Telling Yourself

2021· article· en· W4200398421 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
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCollationScope (computer science)Computer scienceProcess (computing)Sample (material)Variation (astronomy)Term (time)Information retrievalChemistry

Abstract

fetched live from OpenAlex

Although textual scholars agree that collation is a crucial component of the editing process, it often goes undefined and only briefly explained. This article defines the term, explains different kinds of collation, and explores some of its applications. We emphasize stemmatology and medieval textual traditions. By drawing from editorial examples and the theoretical frameworks of projects centred on works such as the Canterbury Tales, Troilus and Criseyde, Dante’s Commedia and the Greek New Testament, the article seeks to compare manual and computer-assisted approaches to collation methods. We delineate the scope of this activity and argue that computer-assisted collation minimizes the risk of missing out on relevant data. We examine the advantages of full-text collation over sample collation and conclude that no decisions about stemmatically significant variation can be made a priory and that variant distribution is the major factor weighing on significance.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.902
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0040.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.084
GPT teacher head0.263
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