REBECCA BELMORE AND JAMES LUNA ON LOCATION AT VENICE: THE ALLEGORICAL INDIAN REDUX
Bibliographic record
Abstract
At the Venice Biennale of 2005 Rebecca Belmore's Fountain and James Luna's Emendatio , although presented in different locales, converged for reasons that knowingly complicated the fact that both artists are Native North Americans. Their works were allegories about, as well as for, the location: reliant on Venice as a city of allegorical certainty; reliant on allegories, skewed and traduced, about the Native. Venice as its own allegory was both reiterated and disturbed by them, Venice also the container of countless allegorical tellings – on plinth, roundel and triptych, on wall, floor, ceiling and on roof – of the stories that held the whole operation together, the engines of its power. Many of them were re‐tellings of the moral systems of other great powers – Greece, Rome, Byzantium – conjoined as the allegorical force of Christendom. It is suggested that on location at the Biennale, a hub for cognoscenti with some collective memory for reappraisals of allegory by Walter Benjamin, Paul de Man or James Clifford, Fountain and Emendatio are allegory‐adjusted and in progress: that they are not so much stories as episodes, circular iterations, repeated suggestive disclosures – repeated, but restricted, demanding to know whose allegories, if any, are reliable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".