Re-negotiating Exhibitionary Practices and the "Digital" Politics of Display: The Case of the MTL Urban Museum App
Bibliographic record
Abstract
In this paper, I employ a sociotechnical approach (drawn from science and technology studies) to reconstruct how the McCord Museum’s MTL Urban Museum App was re-made. I take into account both the social and the technical, and consider the human and the nonhuman, which allows me to chart the roles of heterogeneous actors in re-making the App and in re-negotiating the Museum’s display practices. In doing so, I explore and point to the politics of this 'digital' display: What actors were involved in its re-making? How did they participate in decision-making processes? What are the implications of the negotiations made? The analysis reveals: 1) how the re-making of the App redistributed tasks associated with exhibitionary practices by displacing them across unexpected actors both inside and outside the Museum, 2) how some aspects of design can become ‘non-negotiable’ or ‘irreversible’, and 3) how the re-negotiation of display practices established unanticipated ‘gatekeepers’ in the Museum’s display practice. Thus, this study sheds light on a “digital” case of the ‘politics of display’ (Macdonald, 1998).
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.033 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".