Seize: A Mobile Augmented Reality Walking Game through Critical Making
Bibliographic record
Abstract
The previous zero-Covid policy in China causes the waning of public space and more disciplinary control. Although the policy has changed, the trauma of eroding the individual space and weakening our bodily control still exists. In this project, we will make a mobile augmented reality game named Seize to invite players in Shanghai, China, to create a playful space for accessibility, connection, and sharing emotions collectively in the context of the post-pandemic time. The game is a remembrance and healing of the lockdown. This game is a continuation of a series of virtual games organized during the lockdown in Shanghai from March to June 2022 at virtual meeting platforms for constructing a mutual connection. In this game, walking is the key mechanic and a tool to re-configure, recognize, and re-experience the urban landscape. It is an expansion to the physical space for addressing the restrictions of the Chinese social, cultural, and political context. The process of making inherits the methodology of critical game design. The game playtesting will invite the locals in Shanghai to play, evaluate, discuss, and share collectively as a community. This project is inspired by the long history of walking as a form of protest from Walter Benjamin's writing about Flneur to later International Situationalist movement. City is remapped and reimagined through walking which creates a different sense of time and space. The emerging of locative media in recent decades leveraged by game designer to remap the reality with the mediation of mobile technologies. Mary Flanagan(2009, 6) defines critical play is "to create or occupy play environments and activities that represent one or more question about aspect of human life." This project will be situated in the framework of critical game which addresses the issue of the reality of game. Seize is not only a project to reflect the reality, but intervene and hack into the Chinese current political and cultural discourse through playful activity enacted in the city. We will present the process of our game design, playtesting/workshop, iteration, and final gameplay organized in Shanghai.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".