The Booze Cruise: Impaired Driving in Virtual Spaces
Bibliographic record
Abstract
In January 2007, the University of Calgary offered the first Canadian course in Serious Game Development.1,2 Computer Science 701.03 was officially a high-level graduate course, but it included participants from the arts and interdisciplinary studies at senior undergraduate levels. The course evaluation was based on a game project. The class selected a simulation of impaired driving, and called it the Booze Cruise. Before embarking on the simulation, we consulted with the Calgary Police Service. Its alcohol unit was keen to help and gave a summary of the what and why of accident types related to alcohol consumption. Impaired driving is a serious problem and a preventable cause of death and injury. Private and public health organizations are always seeking better ways to reach the public with the message that drinking and driving is dangerous and socially irresponsible. When we announced the game in October 2007, the media response was huge (see Figure 1). The game also won the best student game and people's choice categories in the FuturePlay 2007 Conference's games competition that year (www. futureplay.org). In this article, we summarize the game's design process and development stages. We also look at media responses, which are still active.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".