Performer la collection. Comment le reenactment performe-t-il ce qu’il recrée ?
Bibliographic record
Abstract
Les recherches récentes en histoire de l’art et en muséologie relient les pratiques du « re- » à l’histoire de la performance et de l’exposition ainsi qu’à un engouement pour les retours vers le passé, en leur attribuant une fonction mémorielle ou d’hommage. Mais le reenactment n’implique pas seulement la remise en acte d’une oeuvre en soi; son processus d’actualisation remet également en jeu des mécanismes historiques et discursifs. Cet article tente de redéfinir et d’approfondir cette notion en art contemporain à partir de trois études de cas : Nachbau (Reconstruction) de Simon Starling (2007), Just Pompidou it. Rétrospective du Centre Pompidou d’Alexandra Pirici & Manuel Pelmus (2014) et Artist Tour Guide de Maria Hupfield (2014). En travaillant avec des collections muséales, ces artistes opposent la pratique du reenactment à un système figé, linéaire et hiérarchisé, et pointent la nécessité de renouveler continuellement la relation du musée à l’histoire. Si le reenactment a le potentiel de « performer la collection », c’est non seulement en raison de sa performativité, déjà reconnue par plusieurs auteurs, mais également de sa réflexivité et de sa temporalité anachronique, que nous désignons ici comme sa double historicité.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 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".