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Record W6930166222 · doi:10.5281/zenodo.10881268

MyNDIR: My Norse Digital Image Repository

2024· article· en· W6930166222 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.

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typearticle
Languageen
FieldEngineering
TopicMaterial Properties and Processing
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSpellSelection (genetic algorithm)IcelandicAcronymPhilologyDigital libraryArtifact (error)

Abstract

fetched live from OpenAlex

MyNDIR is the creation of Trish Baer, an adjunct professor in Medieval Studies at the University of Victoria. Site programming is by Martin Holmes, and design is by Pat Szpak, both of the University of Victoria Humanities Computing and Media Centre. The acronym MyNDIR stands for My Norse Digital Image Repository and the letters that it is comprised of spell the Icelandic word for "pictures." The critical approach for the selection of illustrations is focused through the theoretical lens of Material Philology which considers books and their material details, such as covers and illustrations, as cultural artifacts. This selection criteria results in a repository of images that is capable of revealing aspects of book history, culture, and production that the words of the texts alone cannot provide. Consequently, iterations of illustrations with minimal differences are not only included but valued for their research potential, e.g., illustrations from the first and second editions of Kongesagaer published in 1899 and in 1900.

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 categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.927
Threshold uncertainty score0.998

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 communication0.0030.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.007

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 head0.014
GPT teacher head0.198
Teacher spread0.184 · 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