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Record W2888549278 · doi:10.1177/1527476418794634

Guided by Delight: Music Apps and the Politics of User Interface Design in the iOS Platform

2018· article· en· W2888549278 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTelevision & New Media · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsMcGill University
Fundersnot available
KeywordsMusicalInterface (matter)NormativeComputer scienceUser interfaceQueerConventionHuman–computer interactionSoftwareMultimediaWorld Wide WebSociologyVisual artsArtEpistemology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.314
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it