Engineering culture: logics of optimization in music, games, and apps
Bibliographic record
Abstract
This article investigates the ways content producers, marketers, and other promotional stakeholders work to optimize cultural goods and services for platform-dependent production, distribution, and monetization. We are particularly interested in how content creators find novel ways to work within, around, and even against platform politics and policies by manipulating algorithms, business models, and guidelines, or otherwise readying their content for optimal circulation on multiple platforms. Through comparative cases of music, games, and apps that draw on trade press and industry discourse, institutional and financial analysis, and select interviews with musicians, we consider various forms of, and strategies for, what we call cultural optimization. We draw on these instances to better understand the similarities and differences in the optimization of cultural content and metadata for economic or cultural gains. We hope our comparative approach reveals different conceptions of the term optimization, and that this term—in all its digital, financial, and cybernetic connotations—might prompt new ways of thinking about the interactions between content, (meta)data, platforms, and culture that have long shaped the circulation of cultural goods.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.066 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".