Guided by Delight: Music Apps and the Politics of User Interface Design in the iOS Platform
Bibliographic record
Abstract
Seemingly trivial software does important cultural work, both reflecting hegemonic norms and providing opportunities for transforming them. Software applications for music production (music apps) within the iOS app store promise to broaden the potential for musical participation through simple, “fun,” user-friendly interface design. Yet, within the dominant user interface convention, “fun” is synonymous with the experience of instant success and effortless musical mastery. Drawing on semistructured interviews conducted with developers, and an analysis of shared user interface design conventions across three case studies of apps, ThumbJam, iMaschine 2, and Skram, I argue that normative conceptions of human perfectibility are assumed to be what generates an optimal user experience. Exploring theories of “queer fun,” and the importance of “failure” in studies of video gaming, I propose alternative conceptions of “fun,” and consider how, and with what effects, these might be implemented in the world of music apps.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.073 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".