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Record W4231716652 · doi:10.32920/ryerson.14655777

Things As Yet Unknown : a Finding Aid for Selected 8 x 10 Negatives from the Roger Mertin Archive

2021· preprint· en· W4231716652 on OpenAlexfundaboutno aff
Adam G. Ryan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicPhotography and Visual Culture
Canadian institutionsnot available
FundersNSCAD UniversityUniversity of Northern IowaFlorida State UniversityVirginia Commonwealth UniversitySyracuse UniversityUniversity of LouisvilleMassachusetts Institute of Technology
KeywordsNegativeGeorge (robot)PhotographyArt historyVisual artsPoint (geometry)Field (mathematics)ArtLibrary scienceOperations researchHistoryComputer scienceEngineering

Abstract

fetched live from OpenAlex

This thesis and project centers on a portion of the 8x10 inch negatives of the Roger Mertin Archive at George Eastman House, International Museum of Photography & Film, in Rochester, New York. Roger Mertin, a once‐prominent photographer, came of age artistically during a turning point in photographic history—he widespread “cademization” of the field. Sorely under‐researched, Mertin’ work remains a critical example of an aesthetic attitude exhibited by a number of influential photographers from his generation. Since taking custody of the Archive, GEH has kept it in storage, relatively undisturbed. Throughout most of 2012, an item‐level spreadsheet was compiled and the objects were catalogued and given accession numbers. In satisfaction of the requirements for a Master’ degree in Photographic Preservation and Collections Management from Ryerson University, this thesis outlines, discusses and defends my methodology. The resulting finding aid also includes appendices thought to be useful to current and future researchers.

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)0.003
metaresearch head score (Gemma)0.007
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: Other · Consensus signal: Other
Teacher disagreement score0.067
Threshold uncertainty score0.224

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.004
Science and technology studies0.0040.001
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0670.031

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 head0.053
GPT teacher head0.285
Teacher spread0.231 · 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
GenreOther

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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