Risk and Capitalist Power: Conceptual Tools for Studying the Political Economy of Hollywood
Bibliographic record
Abstract
In this article, the structure of Hollywood film distribution will be analyzed through the lens of risk. In both its technical and conceptual senses, risk is relevant to the study of Hollywood’s dominant firms. In the interest of lowering risk, the business interests of Hollywood look to predetermine how new films will function in an already instituted order of cinema, which includes the creativity of filmmakers and the habits of moviegoers. This presentation of risk will explain why, for the political economy of Hollywood, the social world of cinema is an instrumental order. While risk is specifically about the size and pattern of future earnings, it is also an indirect prediction about the stability of the social conditions that would help translate potential earnings into an actual stream. The social world of cinema has a bearing on the Hollywood film business’s degree of confidence, which refers to the ability of capitalists to make predictions about future earnings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".