Chinatown Time Machine: A Research Creation Project on Game Design and Cultural Heritage
Bibliographic record
Abstract
One of the largest Chinatowns in the world, located downtown and sprawling out from the intersection of Dundas Street and Spadina Avenue, Toronto's Chinatown has drawn immigrants from southern China and Hong Kong, and more recently from mainland China. It has a deeply textured history marked by geographic and ethnic shifts as well as government expropriation. How do we remember these layered and sometimes obscured histories and stories? This Major Research Paper provides answers by creating a theoretical model intersecting theories of cultural memory with theories of game studies for the design of a research creation project focussed on Chinatown's cultural history. Chinatown Time Machine is a serious game with educational and cultural value that aims at exploring and sharing the history of Toronto Chinatown and examining a new form of digital archive as inspirations for future cultural recuperations. Equipped with augmented reality (AR) technology, and wearable devices, Chinatown Time Machine allows players to gain an immersive virtual experience at the core of Toronto city and travel to the time they selected to live the life of residents at Toronto's Chinatowns and witness significant events in the history of Toronto Chinatown. For example, players may be able to experience the Chinese Victory Celebrations parade at Chinatown if they chooseAugust 26, 1945, on Elizabeth Street. The concept of this serious game is to guide players to explore the stories behind the change of the city. This project helps address two key issues of technology knowledge feasibility, as well as the need for technology savvy staffing. Hence, in this game design document I focus on promoting the history of Toronto Chinatown along with the history of Chinese immigrants in Toronto. Players will take the time machine, travel back in history, and immerse in the life of individuals at different historical times in Toronto Chinatown.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".