Bibliographic record
Abstract
The Kerameikos project invited seven Australian ceramicists and mixed-media artists - Monica Rani Rudhar, Janet Fieldhouse, Idil Abdullahi, Vipoo Srivilasa, Juz Kitson, Kirsten Coelho, and Glenn Barkley - to explore the expansive historic collections of the Chau Chak Wing Museum and create new works from their experience. The provocation to reimagine collections became the framework for these contemporary artists to address institutional legacies, colonial collecting practices, and current social challenges, resulting in a unique exhibition that reconceptualises a collections-based exhibition. In Greek antiquity, the Kerameikos was the potters’ quarter, where craftspeople and artisans came together to produce some of the finest ceramics of the Mediterranean region and was a hub of innovation for the already ancient art form of ceramics. Key to the development of Kerameikos at the CCWM was a research-intensive week together at the Museum, creating a new hub of innovation, where the artists engaged with each other, our museum team and the collections. The installed exhibition offers a new way of seeing Australia's oldest university collections, challenging historical narratives and infusing the galleries with culturally diverse, and non-academic, perspectives, that reflect our contemporary museum communities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.307 | 0.053 |
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".