Pratiques interactives et immersives ; pratiques spatiales critiques. La réalité augmentée de l’espace d’exposition (with an abstract in English)
Bibliographic record
Abstract
[Interactive and Immersive Practices; Critical Spatial Practices. The Augmented Reality of the Exhibition Space] The rise of installations, as well as immersive and interactive spaces, in both art and science museums has accustomed the public to heightened interactivity, leading to a better understanding of social, natural and scientific phenomena. These spatial systems have also paved the way for the production of innovative environments within exhibition design. This article aims to present a brief overview of the origins of immersive and sensory practices at work in contemporary museums, and to evaluate the potential of these processes to generate critical thinking in the visitor. A concise description of the evolution of the operational and intellectual premise of these museums and their changing practices demonstrates the transformations that have occurred, their modalities, and their application in selected works, notably: The Weather Project by Olafur Eliasson presented at Tate Modern London; Diller + Scofidio’s media pavilion, Blur Building, in Yverdon-les-Bains in Switzerland; and the exhibition Sense of the City at the Canadian Centre for Architecture in Montreal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.045 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".