Bibliographic record
Abstract
A free ebook version of this title is available through Luminos, University of California Press's Open Access publishing program. Visit www.luminosoa.org to learn more. Sequels, reboots, franchises, and songs that remake old songs—does it feel like everything new in popular culture is just derivative of something old? Contrary to popular belief, the reason is not audiences or marketing, but Wall Street. In this book, Andrew deWaard shows how the financial sector is dismantling the creative capacity of cultural industries by upwardly redistributing wealth, consolidating corporate media, harming creative labor, and restricting our collective media culture. Moreover, financialization is transforming the very character of our mediascapes for branded transactions. Our media are increasingly shaped by the profit-extraction techniques of hedge funds, asset managers, venture capitalists, private equity firms, and derivatives traders. Illustrated with examples drawn from popular culture, Derivative Media offers readers the critical financial literacy necessary to understand the destructive financialization of film, television, and popular music—and provides a plan to reverse this dire threat to culture.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.504 | 0.298 |
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".