Bibliographic record
Abstract
Digital games have matured substantially as a narrative medium in the last decade. However, there is still much work to be done to more fully understand the poetics of story-based-games. Game narrative remains an important issue with significant cultural, economic and scholarly implications. In this article, we undertake a critical analysis of the design of narrative within Mass Effect 2: a game whose narrative is highly regarded in both scholarly and vernacular communities. We follow the classic humanities methodology of “close-reading”: the detailed observation, deconstruction, and analysis of a text. Our close-reading employs a critical framework from our previous work to isolate and highlight the central narrative design parameters within digital games. This framework is grounded in the scholarly discourse around games and narrative, and has been tested and revised in the process of close-reading and analyzing contemporary games. The narrative design parameters we examine are character, storyworld, narrativized interface, emotion, and plot coherence. Our analysis uses these parameters to explicate a series of design decisions for the effective creation of narrative experience in Mass Effect 2, and by extension, for game narratives in general. We also expand our previous methodology through a focused “edge-case” strategy for exploring the limits of character, action, and story in the game. Finally, we position our analysis of Mass Effect 2 within contemporary discourses of “bounded agency”, and explore how the game negotiates the tension between player-expression, and narrative inevitability to create opportunities for sophisticated narrative poetics including tragedy and sacrifice.
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".