Games, narrative and the design of interface
Bibliographic record
Abstract
There is a potential disconnection between the experience of narrative and the active decision-making necessary for successful gameplay. Gameplayers must oscillate between a hypermediated participation in game decisions, and the transparent pleasure in the narrative frame of the game (Bolter and Grusin, 1999; Manovich, 2001). This paper analyses one critical locus for facilitating player oscillation and bridging the gap between narrative pleasure and gameplay interaction. Narrative dynamics can be designed directly into the focus of active gameplay – the game interface. This paper identifies and explicates four separate design approaches for integrating narrative within the game’ interface: (1) a narrativised ‘look and feel’ of the interface; (2) behavioural mimicking and behavioural metaphors; (3) narrativised perspective and (4) ‘bridging’ and mixed-reality interfaces. These concepts are useful for describing, analysing and understanding how narrative experience can be instantiated within the game interface. Application of these concepts can help to reveal useful strategies for conjoining ludic play with narrative pleasure. Collectively, this approach is a step towards creating a common theoretical vocabulary for discussing the phenomenon of narrativised game interface.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".