“You’re Getting to be a Habit with Me”: Diegetic Music, Narrative, and Discourse in Bioshock
Bibliographic record
Abstract
In 2K Games’ Bioshock (2007) the player, as the protagonist Jack, is thrown into a dystopian, futuristic alternate history of America. Rapture is an underwater city saturated in music: popular songs from the mid twentieth century; classical-style soundtrack pieces composed by Garry Schyman; characters humming, singing, whistling or playing instruments; musical vending machines; and even the sounds of whales and other creatures all participate in forming a textured soundscape. The songs from the 1930s - 50s used throughout Bioshock recall a real-world cultural environment—a popular music culture that is both comfortably recognizable yet strangely unfamiliar. They occur within the game world and are heard by the player and game characters, and thus the songs are diegetic or “screen music.” In Bioshock, such music is an explicit component of narrative production, game environment creation, and player immersion. Significantly, diegetic music participates in the construction of narrative through a constant interplay or negotiation with the video game’s other elements—visual, textual, ludic—and ultimately functions as a distinct discourse able to mediate for Jack/the player between contesting factors, via established conventional codes of musical, cultural, film, and now video game signification. Bioshock’s use of music initiates a pre-game discourse during installation and prior to every game session in the disc-loading scenes, and this musical discourse is continued throughout the narrative. The story’s opening and descent into Rapture further establishes and “naturalizes” the presence of diegetic music as part of the story being told, and as a vital component of the audio-visual environment enhances player immersion. At the same time, these opening instances and subsequent occurrences of diegetic music at significant points in the story demonstrate that music’s culturally encoded emotive potential produces ironic and poignant effects, while its lyrical intertextuality generates narratological and ludic commentary in various song/scene pairings.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".