How Dangerous Are Virtual Worlds Really? A Research Note on the <i>Statecraft</i> Simulation Debate
Bibliographic record
Abstract
This brief article weighs in on a pedagogical debate concerning the didactic usefulness of an online international relations computer simulation called Statecraft. In a 2014 article, Gustavo Carvalho, a teaching assistant at the University of Toronto, claimed, based on the results of a survey he administered to an international relations class that used Statecraft, that the simulation had little to offer students as a teaching tool. In a rebuttal, Statecraft creator Jonathan Keller took Carvalho to task for not employing the simulation properly, which biased his results. While Carvalho only presented results for one class, the present analysis reports on survey responses of students over six different classes which used Statecraft from 2013 to 2014. The results call into question Carvalho’s findings and suggest that the context and curriculum matter as much as the simulation itself when judging the pedagogical value of computer-mediated learning tools.
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| 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".