Analyzing Moral Deliberation During Gameplay: Moral Foundations Theory as an Analytic Resource
Bibliographic record
Abstract
This article explores the role of interplayer moral conversation in multiplayer games with three subquestions: how to design and use games for morality research, how advances in moral theory can inform game-based research into morals, and how game-based research can inform moral theory. A long tradition has investigated morals using games such as Ultimatum and Dictator; however, this research often omits interplayer moral dialogue. Further, when moral foundations theory is accounted for, analysis of these games seems to investigate a narrow range of moral reasoning. In this methodological critique, we draw upon data from gameplay of a simulation of climate change debate and find a wide range of moral foundations through analysis of dialogue. Our analysis suggests that in-game player dialogue is a source of rich moral deliberation and potential for using simulation games as grounds for discovering new moral foundations.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".