Dépasser le modèle du musée irénique. L’étude du processus de cocréation d’un dispositif de médiation numérique pour les salles des arts du Tout-Monde au Musée des beaux-arts de Montréal
Bibliographic record
Abstract
Examining participatory practises and inclusion projects requires reflecting on the museum as a political space and questioning the power relationships it nourishes. A review of the literature on the idea of the sense of belonging in a wide variety of disciplinary fields led to an awareness of the different dimensions of that feeling and its integration in the political discourse. More specifically, in terms of this article, the feeling of belonging is problematic in the context of art museums, where the concept is often translated to the institution’s benevolent attitude towards racialized groups that have historically been discriminated against and are not in the habit of visiting museums. When these people are invited to collaborate with the museal institution, the co-creation process generally proceeds according to codes established by the institution. This tendency to adopt a charitable position towards the Other, avoiding any sort of tension, is what we call “a irenic museum” (from the Greek εἰρήνη [eirnê] meaning “peace”). By focusing on what unifies rather than on what divides, the irenical approach avoids conflict in order to foster coexistence with one another. By analyzing the process of co-creating a digital mediation system for the Arts of One World galleries at the Montreal Museum of Fine Arts (MMFA), we suggest a critical look at the design thinking approach centred on the human at PRISM, the museum’s Digital Mediation Innovation Lab. Can we move beyond the limits of the peace museum by promoting equal interaction between those involved?
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.048 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".