Avatarme: digital avatars in a theme park queue creating a better experience and an emotional connection
Bibliographic record
Abstract
Research has shown that through customization, a user can create a longer and more meaningful connection with their avatar. This research harnesses this emotional bond in order to create a more engaging experience in the queue at theme parks. Two methods were used to collect information. The first was an online survey about queuing, Bitmoji, and theme park behavior. The second was an on-site observational study to gain a more detailed look into guest behavior in a theme park queue. This research concludes with mockups and documentation for implementing a park-wide avatar system for alleviating queue boredom for the end user. Queues are not only for holding guests in a fair line, but for on-boarding, for the immersive story and background, and for entertainment. By personalizing and customizing the experience for the guest, a more enriching and emotional connection can be made to the attraction, and the park itself.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".