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Record W7047208494

Gabor Szilasi : The Art World in Montreal, 1960–1980

2019· book· en· W7047208494 on OpenAlexaboutno aff

Bibliographic record

VenueE-Artexte (Artexte) · 2019
Typebook
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsExhibitionPhotographyThe artsNegativeContemporary artArt worldReflection (computer programming)
DOInot available

Abstract

fetched live from OpenAlex

"Born in Hungary in 1928, Gabor Szilasi is one of Quebec's best-known living photographers. Soon after settling in Montreal in 1959, Szilasi began photographing the many art openings that he regularly attended with his wife, artist Doreen Lindsay. Over the next two decades he produced an extensive photographic record of the individuals who comprised Montreal's visual arts community, a number of whom would shape the history of art in Canada. \n \nExpanding on a solo exhibition of Szilasi's photographs that took place at the McCord Museum in 2017, the book features three essays, an interview, and over one hundred images that capture, with characteristic candour, perspicacity, and wit, some of the radical changes that affected Montreal's art world throughout the 1960s and 1970s. Szilasi's significant body of work - totalling approximately 3,600 negatives - provides a rare look at the social lives of Canadian artists during a time of great effervescence and creative possibility. Gabor Szilasi: The Art World in Montreal invites reflection on what has since been lost and gained." -- Publisher's website.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.177
Threshold uncertainty score0.356

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.004
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.004

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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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
Published2019
Admission routes1
Has abstractyes

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