An Alternative Art Museum Engagement in the Era of COVID-19 Pandemic
Bibliographic record
Abstract
The social distancing measures during the COVID-19 pandemic impacted our lives in many ways, whether psychologically or physically. While the lockdown influenced our social existences, embodied interactions, ways of communication, and perception, it could offer an opportunity to think about the possibilities of alternate spaces. Provoked by the new normal, we adopted an a/r/tographical experimentation based on the following proposition: Physically go to a museum by taking another person virtually. For us, this proposition became an entry to discover an exploratory environment which led to re-examine our positionality as artist-educators while acting and reacting to the physical space of the museum and virtual spaces of our devices. In this a/r/tographical study, we ask: How does the process of knowledge-making vary between the online and physical space of the art museum? In what way do the teaching and learning approaches reshape from in-person to virtual and vice versa? And how does a/r/tography as methodology could accommodate activating the potential in-between spaces of the virtual and actual? We explore our inquiry at the art museums located in Chicago and Vancouver. We visited each museum in-person and through video calls; then, exchanged documentation of our experiences through conversations and corresponding writings. Although this co-generated aesthetic experience appears different from regular gallery and museum visits, in the ambiguity of this journey, our thinking proceeds and stretches the exhibition space to an inquiry in which we share our thoughts and perceptions towards new ways of learning. This creative scholarship has become an allegory for the relationality and communication between educators and learners. By relying on each other’s bodies and perceptions, we adopted the way in which learners trust educators and educators become accountable for navigating physical and online spaces of learning.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.020 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".