The Promise of New Museum Models in a Moment of Social Reckoning
Bibliographic record
Abstract
This chapter examines the shift from in-person to digital engagement at North America's fourth largest museum, the Art Gallery of Ontario (AGO), in the context of a major moment of sociocultural reckoning. We track learning in relation to programme innovation by asking: What resonates with audiences? What is the process of and reception for original performance commissions? What is the measurable potential for both delivery of on-site and online content? In our view, a telling moment in this space of new delivery is the perceived and real understanding of what a “live” event constitutes. Amongst our offerings, for example, is the fabrication of a live event: an event that is pre-recorded and promoted live to screen. Is the fabrication of the live event a transitional moment, or may it resonate as a bona fide museum presentation modality? While situating this chapter in a specific pandemic moment, it has accelerated our need to ask broader questions around change and liveliness and what the future museum looks like. We propose the following four areas for consideration: 1) evergreen resources: what they are and how they represent liveliness through sustained and upward curve metrics over time; 2) Director Talks: who are our leaders and how do they support and foster a moment's relevancy; 3) centring artists, so that we can gather and unite many voices and perspectives through art; and 4) skeuomorphic design, or the spark to imagine the museum anew.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".