Bibliographic record
Abstract
Museums enrich our lives. By presenting knowledge, culture, history and more, the museum experience can add interest and fun to our day. But how do people experience museum visits? Moreover, how can museums stimulate people to return? Augmented reality (AR) can intensify a museum experience and contribute to learning, education and the training process. People can learn more easily when they are active and involved with their environment. Traditionally, most visual arts do not require the visitorsʼ active engagement, aside from looking and thinking about the artworks presented. As a result, there is a limit to the artworks ability to transfer only a fraction of the information that can be transferred. When people are actively engaged in an educational experience, there is potential for them to develop an emotional experience. AR can be a new way to enrich a museum experience and encourage visitors to return. This Thesis research explores the design of an interactive educational installation using AR. A is a growing field, AR enables the user to simultaneously explore their physical surroundings while accessing computer-generated visual data. This research was done in collaboration with the Banff Park Museum (BPM) National Historic Site of Canada. The museum has been in existence for over 100 years, and it mainly holds preserved taxidermy specimens as well as some geological artifacts and historical documentations. Although the museum holds historical significance, it struggles to keep its relevance to some modern day audiences. Furthermore, the taxidermy elicits a negative emotional response for a number of visitors, as values and traditions change over time. This research suggests a set of guidelines for creating a system based on AR for a handheld device for easy use in the museum. The AR system would give the visitors additional computer-graphic information about the specimens presented in the museum. By building a museum theory, and by using guidelines to create the AR system, this research hopes to increase in the future the visitorsʼ interest in the museum content. Furthermore, as a result of increasing the visitorsʼ interests, it is hoped that those visitors who previously recoiled from the taxidermy would now have a more positive emotional response, or at the very least - acceptance.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.008 |
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".