Performing Corporate Culture: Analysing Meta-Narratives and Online Interactivity Through Quigital
Bibliographic record
Abstract
Home Comfort Advisor is a collaborative composition for online choir, presented by the fictional corporation ‘Quigital’. This paper details the work's co-opting of corporate aesthetics, analysis, productivity, design strategies and their implications in musical language, collaboration, performer agency and audience reception. The resulting choir performance masquerades as an interactive online product launch, making unconventional analytical demands on individual choir members as singers, community members, distributed decision makers and content creators. To manage the logistical challenges presented by an interdisciplinary collaboration featuring 70 agents and a creative development team, linear approaches to composition were replaced by a modified iterative design loop. (Collaborative creation ⇒ analysis ⇒(re)-interpretation ⇒ interactive audience engagement: repeat/deploy:). More specifically, this paper examines emergent materials, structures and processes used to amplify collaboration. Home Comfort Advisor's deeply embedded corporate aesthetic and technological infrastructure reveals novel interactions between the score, text, code, design assets, analysist and performer. An analysis of the non-linear, collaborative relationships provides a model for creating similar data-driven, interdisciplinary collaborations. Quigital's manipulation of a mutually understood code hidden in plain sight approaches what Limor Shifman refers to as hypersignification. This cultivation of technologically amplified, collaborative metanarrative lies at the heart of Quigital's success and its ability to be both approachable, subversive and deeply disturbing.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".