Game design strategies for collectivist persuasion
Bibliographic record
Abstract
A fundamental feature of serious games is persuasion, an attempt to influence behaviors, feelings, or thoughts. Much of the existing research on serious games and, more generally, on persuasive technology (PT), does not address the important links between persuasion and culture. It has tended to originate from Western, individualist cultures, and has focused on how to design for these audiences. In this paper, we describe the design of one of two versions of a serious game we developed about quitting smoking titled Smoke? which is targeted at collectivist players. We show how the design was informed by persuasive strategies we identified from the cross-cultural psychology literature, intended for use in games for players of collectivist cultures: HARMONY, GROUP OPINION, MONITORING, DISESTABLISHING, and TEAM PERFORMANCE. We then discuss the results of a quantitative investigation of the effects of both game versions on both individualist and collectivist players.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".