Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".