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Record W4404477331 · doi:10.1525/9780520422742

The Photographic Object 1970

2016· book· en· W4404477331 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldArts and Humanities
TopicPhotography and Visual Culture
Canadian institutionsnot available
Fundersnot available
KeywordsObject (grammar)Computer scienceComputer graphics (images)ArtComputer visionArtificial intelligence

Abstract

fetched live from OpenAlex

In 1970 photography curator Peter C. Bunnell organized an exhibition called Photography into Sculpture for the Museum of Modern Art, New York. The project, which brought together twenty-three photographers and artists from the United States and Canada, was among the first exhibitions to recognize work that blurred the boundaries between photography and other mediums. At once an exhibition catalogue after the fact, an oral history, and a critical reading of exhibitions and experimental photography during the 1960s and 1970s, The Photographic Object 1970 proposes precedents for contemporary artists who continue to challenge traditional practices and categories. Mary Statzer has gathered a range of diverse materials, including contributions from Bunnell, Eva Respini and Drew Sawyer, Erin O’Toole, Lucy Soutter, and Rebecca Morse as well as interviews with Ellen Brooks, Michael de Courcy, Richard Jackson, Jerry McMillan, and other of the exhibition’s surviving artists. Featuring seventy-nine illustrations, most of them in color, this volume is an essential resource on a groundbreaking exhibition.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.082
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.004
Scholarly communication0.0080.005
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0820.023

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 head0.027
GPT teacher head0.235
Teacher spread0.208 · 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
Published2016
Admission routes1
Has abstractyes

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