Year Zero Economics - Using Edu-Larping to Explore Economic Systems in the Ninth Grade
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The curriculum of the province of Alberta, Canada, stipulates that grade nine students learn about economics, comparing the United States and Canada (Alberta Education 2007). Supervisor Ken Koziej assigned Mikael Hellström to teach this unit to three grade nine classes during his practicum. Hellström had pioneered the use of edu-larps, game-based learning, and gamification as a sessional instructor at the Universities of Alberta and Calgary (Hellström 2016; 2017) and chose those methods to deliver the unit. The game design for the tabletop role-playing game Mutant: Year Zero (The Free League 2015) incorporates mechanics for play on three levels: character, for world exploration; resource extraction and world exploration; and base building. The latter two levels effectively create an in-game economy. Collaborating with his supervisor, Hellström adapted them for classroom use. The goal was to create a unit fulfilling Mochocki’s (2014) criteria for edu-larp, in other words, that it is a) mono-disciplinary and targets a single school subject; b) knowledge-oriented and communicates textbook subject matter to students; and c) teacher-friendly, by not demanding time-consuming preparations. This paper describes the unit, the process of converting elements of Mutant: Year Zero for teaching, how students played the unit, and the post-game evaluation. While the unit did not fulfill all of Mochocki’s criteria, student engagement was high, consistent with previous findings on game-based learning (Prensky 2005; Gee 2007; Hattie 2009).
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.
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 | 0.001 | 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 | 0.001 | 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 it