Gali’s Prize: \nA Treasure Hunt Game for \nthe Textile Museum of Canada
Bibliographic record
Abstract
Gali’s Prize is an experimental treasure-hunting game that integrates tangible and multi-screen interactions. The game has been designed for the Textile Museum of Canada (TMC) to replace the old quiz-style scavenger hunt with paper and pen. Its goal is to provide an entertaining, educational experience for children on school trips. The learning journey begins with an initial engagement at the starting spot and continues by approaching and connecting with a couple of specific artifacts in the exhibition space. The whole experience blends self- directed curation with an augmented reality (AR) treasure-hunting experience. During their participation, children will learn the stories behind the artifacts they encounter and gain lasting memories of their visit. \nThe investigation stands at the intersection of museum business, children’s learning experience, and digital technology, and explores the opportunities and challenges involved in using mixed technologies in museums and galleries during the near future. At the same time, this examination studies the engagements and interactions of visitors on site. These explorations can potentially create benefits for both museums and visitors. The prototype of Gali’s Prize was inspired by theoretical conclusions in existing literature, personal experiences in museums and galleries, and some studies of particular cases. It helps a specialized museum, the TMC, to experiment with a new solution that may solve their current issues. This paper explains the relevant critical thinking, documents the development process of Gali’s Prize, and provides discussion and reflection about the work.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.005 |
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".