The Design of a Location-Based Transit Game for Digital Placemaking
Bibliographic record
Abstract
Urban residents often use public transit to travel throughout the city yet find it difficult to learn about events in one's neighborhood. Transit rides can also be isolating and routine, despite seeing the same people regularly. As a result, there are opportunities to connect with others on the same route. While digital technologies such as community systems, social media, and public displays have been studied to understand how people engage with each other in their community, little is known about the challenges people face when searching for local information while commuting. Our research explores how one form of technology, location-based games (LBGs), supports urban commuters in digital placemaking. We present a prototype of an LBG, City Explorer, that allows riders to maintain an awareness of location-specific events and to support the sharing of community information. City Explorer is designed for public transit riders in a metropolitan city to collaborate with other riders, supporting community awareness, and facilitating discussions related to places on their transit route.
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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 | 0.000 | 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".