Vocal Imitation and Linguistic Processing of Super Mario Theme Music by Yoruba Gamers
Bibliographic record
Abstract
An aspect of gaming culture among Yorùbá millennials is the linguistic interpretations of the background music that accompanies the popular video game called Super Mario. The themes of the interpretations are comparable to those of music texts at traditional Yorùbá events. Drawing on the Yorùbá tradition, the account is that the gamers assumed that the background music of the game has a similar function as the music at traditional Yorùbá events. The choice of words in the interpretation is conditioned by the situational contexts where the music is heard in the video game. The results of acoustic analyses show that the interpretations are also determined by mapping the pitch trajectories of the music melodies to the tones of the gamers’ language. Notably, the results of this study suggest that the linguistic processing of music may not only involve phonetic iconicity (Steinbeis and Koelsch, 2011) but situational context and social expectation.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".