Art as Atrocity Prevention: The Auschwitz Institute, Artivism, and the 2019 Venice Biennale
Bibliographic record
Abstract
Although largely overlooked in genocide and atrocity prevention scholarship, the arts have a critical role to play in mitigating risk factors associated with genocide and atrocity. Grounded in analysis of "Artivism: The Atrocity Prevention Pavilion,” the Auschwitz Institute for the Prevention of Genocide and Mass Atrocities’ 2019 Venice Biennale exhibition and drawing from fieldwork, interviews, and secondary research, this article explores why one of the leading NGOs working to prevent future violent conflict would choose to curate an art exhibit at the Venice Biennale and what might be accomplished through such an exhibit. Ultimately, the Artivism exhibit, in its collection and range, provides a canvasing of multiple and directed creative interventions that allow for deeper understanding of how the arts can be used as a tool for mitigating risk factors associated with the prevention of genocide and atrocity in such a manner that has important ramifications for future prevention efforts.
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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 | 0.002 | 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 | 0.010 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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.
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".