Amplifying Player Experience to Facilitate Prosocial Outcomes in a Narrative-Based Serious Game
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 rise and development of serious games have shown promise in addressing critical social issues, including school bullying. However, prior work often compares game-based interventions with the conventional non-game approach, failing to generate insights about which game features should be emphasized to create more effective games. To bridge this research gap, in light of video games’ advantages for creating immersive experiences that benefit persuasion, we created a narrative-based serious game addressing school bullying and conducted two studies (Study 1, <em>N</em> = 130; Study 2, <em>N</em> = 250) to explore the persuasive effects of two game features, respectively player–avatar similarity and in-game control, on player experience (including player–avatar identification, narrative engagement, and empathy) and prosocial intention. We found mixed results subject to player perspective such that only when players took the bully’s perspective did one of the game features—in-game control—successfully create the intended empathy via amplified narrative engagement toward the desirable prosocial intention.
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 | 0.000 | 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) | 0.000 | 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