Adaptation and Diegesis of Pre-Existing Music in Historically-Based Video Games
Bibliographic record
Abstract
The modern state of gaming has seen a variety of interactive narratives play out in a variety of worlds. A major setting of video games is ‘the past’, including historically-set games which are meant to mimic real epochs and places, all with varying degrees of accuracy to their factual counterparts. Interestingly, a pattern emerges within the soundtracks of these games: the use of pre-existing, diegetic music. As observed by scholars such as William Gibbons and Andra Ivǎnescu, such music is frequently chosen to make use of an audience’s prior musical associations within the context of the gameworld (Gibbons 2018, 43; Ivǎnescu 2019, 18-19). However, pre-existing music comes with the risk of misaligning with the player’s expectations for a given piece. Additionally, using diegetic music creates an additional risk of misalignment between the observed action and the heard sound (Neumeyer 2009, 31). Combining these two modalities could initially seem to be not worth the collective risks, but games like the historically-based Pentiment (2022) successfully integrate their audio with game narratives through careful curation of pre-existing musical works. What purpose does pre-existing music serve to warrant its widespread inclusion in historically-based games? A comparison of two works from Pentiment, the non-diegetic “Pierro’s Pride” and the diegetic “Rüdeger’s Rehearsal,” will demonstrate how diegetic music is bound by the depicted action, thereby musically reinforcing it, while non-diegetic music takes more liberties in its historical accuracy.
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How this classification was reachedexpand
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".