How Should I Respond to “Good Morning?”: Understanding Choice in Narrative-Rich Games
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
Narrative-rich video games provide opportunities for players to make choices at key points in the game, generating malleability within the game world and its characters. In this study, we explore the types of choices that exist in such games, how choices affect player experience, and how players make decisions when presented with choice. We first conduct interviews with game developers and perform a video observation analysis of existing choices to develop an initial classification system. We then perform a series of semi-structured interviews with video game players to understand how different choices impact player experience. Our findings reveal that choices influence player experience at several levels of meta-gameplay, having impacts on the game itself, the player-game relationship, and the player outside the game. Furthermore, we identify several key factors that affect player decision-making when faced with choice. Finally, we discuss the potential of choice in developing impactful virtual experiences.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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