Rhinoceros: Luxury's Fragile Frontier (Curatorship)
Bibliographic record
Abstract
This exhibition brings together two unlikely companions: the rhinoceros and the city of Venice. It highlights and interrogate the consequences of luxury consumption on an endangered beast and an endangered city. The rhinoceros is the oldest living mammal; walking the earth for over 50 million years. Venice and its beauty have captured the world’s collective imagination for over 1,500 years. Now both Venice and the Rhinoceros have become victims of their desirability and objectification as luxury objects – both consumed without discrimination by an ever-expanding consumer class. The exhibition centres on the sculptures of two artists - Gigi Bon and Shih Li-Jen; a video installation by businesswoman Lynn Johnson; and poetry by the Canadian Ronna Bloom.. Gigi Bon with her Studio d’Arte ‘Mirabilia’ has long seen a profound connection between the rhino and her Venetian home. Taiwanese artist Shih Li-Jen emphasises the connection between the rhino and its consumption in a mainly Asian market. Australian businesswoman Lynn Johnson and her foundation ‘Nature Needs More’ has spearheaded ‘demand-reduction’ campaigns for the consumption of rhino horn in Vietnam and elsewhere, and poet Ronna Bloom writes on fragility and Venice. The event is tied together thematically by the work of Melbourne historian Catherine Kovesi on the longue duree of Luxury and the effects of uncoupling consumption from ethical constraints.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.008 |
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".