Bibliographic record
Abstract
The technological enrichment of museums serves as a prime area of research focused on the changing role of information and communication technologies and the visualization of personal data. While previous research projects have focused on using mobile and interactive technologies as electronic museum guides or tools for viewing additional information, in contrast, this project takes a different approach. It seeks to develop new methods to visualize the data collected from a user's trip through a museum enabling the user to construct a presentation that they can use to reflect, communicate, and share their experiences with others. The visualization they construct is not just a presentation of facts about their trip, but also a visual narrative of what they experienced. To this end, four styles of visual narratives (each targeting a particular purpose) were designed, developed, and tested via a prototype that allowed participants to explore a virtual museum and construct a visual narrative of their trip in any of the designed styles. It also allowed them to access narratives created by others. Our usability study was based upon a series of evaluation criteria such as effectiveness, operability, overall satisfaction, flexibility, and sociability. It demonstrates the users do in fact prefer the proposed narrative visualization and our visualization styles serve distinct purposes. First and foremost, I would like to thank my supervisor Ali Arya. It seems like just the other day I was still in high school going to Carleton University's open house. He was the first person I talked to about the IMD program and he convinced me to go into the undergraduate program that eventually lead to my graduate studies, instead of the video game design stream of the Computer Science program. The IMD program sounded much more interesting and he made it feel really fun and welcoming. It has been quite the journey ever since where I have learned a lot, grown a lot, and really developed the skills, experience, and knowledge to achieve a sense of professionalism. He has been my instructor numerous time and I have worked with him on various research projects. I am
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".