Borrowable Museum: Utilizing Digital Fabrication Techniques for Remote Tangible Experiences
Bibliographic record
Abstract
This thesis aims to create interactive, tangible, and shareable replicas of museum contemporary collections outside their walls. Our work is motivated by the inaccessibility of museums in remote communities that cannot access artworks in-person. Our research questions explore: a) How can we utilize FLAGs to create a tangible public interactive display for museums to serve remote communities? b) How can we design an interface with unbounded interactions, locations and context supported by self-monitoring data collection in the wild? We prototyped the Borrowable Museum (BM), a portable interactive physical display. Using artifacts from the Wearable exhibition at the Canada Museum of Science and Technology, we designed borrowable replicas of three artifacts using digital fabrication techniques. Using insights from the museum, we built the Borrowable Museum to be an interactive screenless box on wheels (with digitally-monitored instruction labels, user logs, and system logs) to house borrowable replicas. We embedded the Borrowable Museum with sensors to record usage online in real-time. We ran a self-monitored study where the Borrowable Museum was publicly displayed for 2 months at 4 different locations (2 recreational and 2 educational). We collected data from 6 installed sensors, 261 hours of field observations, 17 filled questionnaires, and 14 semi-structured interviews to understand how people interact with, make sense of, and perceive such artifacts. All the qualitative data was used to conduct Thematic Analysis and the quantitative data was analyzed for deeper understanding of the time, number, and duration of interactions. Findings show participants’ hesitancy towards handling artifacts outside museums for lack of surrounding context, but familiarity with the space and unmonitored interactions encouraged engagement. Social engagement and online/offline sharing increased among people in groups while empty spaces/hours and unusual labels increased engagement. We then discussed how remote museums and tangible memorabilia added new benefits besides accessibility. We highlight the constraints of shareability and reflect on the research questions by recommending design opportunities for future research.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".