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Record W4252820266 · doi:10.32920/ryerson.14655657.v1

Cataloguing Michael Snow: Photo-Works

2021· preprint· en· W4252820266 on OpenAlexaffabout
Ariel Bader-Shamai

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicConservation Techniques and Studies
Canadian institutionsConcordia UniversityToronto Metropolitan UniversityMcMaster UniversityYork UniversityUniversity of Ottawa
Fundersnot available
KeywordsDocumentationPossession (linguistics)Relation (database)Work (physics)Visual artsSnowPhotographyArtProcess (computing)Computer scienceEngineeringGeographyDatabaseMeteorology

Abstract

fetched live from OpenAlex

This thesis project focuses on a photographic collection of the multidisciplinary artist, Michael Snow (Canadian, b. 1929 - ), which is currently in the artist’s possession and has not been previously studied. The collection includes over 5,000 photographic materials related to Snow’s photo-works and his work in other media. The term photo-work is used in this thesis to appropriately reflect the intermedia character of Snow’s photographic compositions. The first chapter explores Snow’s artistic career and photo-work. Chapter two overviews cataloguing standards in Canada, discusses issues in photographic deterioration, and outlines proper storage techniques. Chapter three discusses the cataloguing process of Snow’s photographic collection, including information about the present condition of these materials, and provides recommendations for its future acquisition and potential use. This thesis argues that insight into an artist’s practice is an important part of the cataloguing process, particularly for collections with materials related to the production and/or documentation of intermedia works. With this knowledge, objects can be better identified and understood in relation to the collection to which they belong and the artist’s overall body of work.

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 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 categoriesInsufficient payload (model declined to judge)
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 score0.809
Threshold uncertainty score0.995

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.058
GPT teacher head0.257
Teacher spread0.199 · 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 teacher head, not a consensus.

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
Published2021
Admission routes2
Has abstractyes

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