Serious hunger games: Increasing awareness about food security in Canada through digital games
Bibliographic record
Abstract
Digital games are becoming increasingly common knowledge transfer media. So-called "serious games" or "games for good" have attracted academic, industry, and mainstream attention through the proliferation of conferences, journals, blogs, and online communities. They offer what few other educational resources can in a single medium: interactive, user-led learning experiences based on discovery and experimentation, explorations of complex systems through skill development and decision making, and a personal connection with the content through role-playing (Bogost, 2007; Dahya, 2009; Gee, 2003; Kee & Bachynski 2009). As digital games move out of the home and into public education, sharing experienced-based insights on how to navigate this new terrain is important and necessary to efficiently create media that is both informative and engaging. This field report reflects on the process of developing the educational game Food Quest, from conception to completion, including the challenges, surprises and lessons learned. After detailing the gameplay of Food Quest, we provide a chronological report on the design and development process, including origins and exploratory phases of the project, concerns around digital game-based learning, and the unanticipated obstacles that contributed to a lengthy development process. The report also provides preliminary evaluations and recommendations for others interested in create a similar digital resource to spread awareness about food security.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".