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Record W7025416179

Wearing Memory: Haji OH

2015· other· en· W7025416179 on OpenAlexaboutno aff

Bibliographic record

VenueResearch Online (University of Wollongong) · 2015
Typeother
Languageen
FieldArts and Humanities
TopicLiterature and Culture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsExhibitionThe artsStudioBachelorFine artContemporary artJapanese artScenographyClothingSubject (documents)
DOInot available

Abstract

fetched live from OpenAlex

Haji Oh has lived in Japan as a third-generation Zainichi Korean. She is an accomplished installation textile artist, researcher and teacher. Oh graduated from Kyoto City University of Arts with a Doctorate of Fine Arts (2012), a Masters of Arts in Fine Arts (2002) and a Bachelor of Arts in Fine Arts (2000). She has participated in Busan Biennale, Korea (2014); VOCA - Vision of Contemporary Art 2012, The Ueno Royal Museum, Tokyo (2012) and Inner Voices, 21st Century Museum of Contemporary Art, Kanazawa, Ishikawa. Most recently, she has presented solo exhibitions at Koganel Art Spot Chateau 2F, Tokyo (2014), and Aomori Contemporary Art Centre (2013). Residencies have been undertaken at York University, York Centre for Asian Research, Toronto; Aomori Contemporary Art Centre; Textile Museum of Canada and The National Folk Museum, Seoul. As a lecturer Oh has taught at Kyoto City University of Arts, and Kyoto University of Arts and Design. In 2014, Oh relocated to Australia from Japan. The project for Oh's residency involves using the gallery as a studio and an installation space to generate a new body of work entitled Wearing Memory. The work explores the subject of memory through mixed media textile practices including weaving, dyeing and stitching as well as fibre construction and deconstruction processes.

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: Other · Consensus signal: Other
Teacher disagreement score0.044
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.113
GPT teacher head0.308
Teacher spread0.195 · 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
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
Published2015
Admission routes1
Has abstractyes

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