How (repeat) museum displays are <i>always</i> experimental: (re-)making MUM and the city-laboratory
Bibliographic record
Abstract
In this paper, I present a case for understanding exhibitionary practices as always experimental. I discuss here a study conducted on the McCord Museum’s MTL Urban Museum App, a digital display that was (re-)made based on the Museum of London’s Streetmuseum App. Drawing on the notion of ‘remediation’ and actor-network theory, I consider the display as formed through the refashioning of an ‘actor-network’, or what I refer to in this paper as an experimental assemblage. This allows me to trace the processes of transformation that brought heterogeneous actors together and into novel arrangements in re-making the App and how such processes resulted in the generation of novel experiences, practices and knowledge. Thus, this study shows that even ‘repeat’ mainstream displays involve experimental processes, or ‘exhibition experiments’. The implication for practitioners in museums, galleries, libraries and other cultural heritage institutions is that decision-making processes must always account for the experimentality of all display practices, ‘new’ or ‘old’.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".