Ostrom’s Razor: Using Bitcoin to Cut Fraud in Hollywood Accounting
Bibliographic record
Abstract
The accounting principles prevalent in Hollywood are seemingly crafted to mislead creators and investors. Film studios and streaming platforms have been found to use complex strategies to annually divert millions in net profits. Many contracts include audit clauses, but the cost of auditing a billion-dollar system is prohibitive for most creatives with “net profit” deals. However, a resourceful minority have recovered billions in profits and damages. We suggest using Bitcoin’s transparent, immutable ledger to eliminate fraudulent accounting and build trust among profit-seeking filmmakers willing to trade maximum income for maximum profit per share. This trust can be spread globally utilizing the Bitcoin network as a transparent and immutable triple-entry accounting system. Our research shows that distributing this decentralized trust is achievable by configuring an ecosystem of existing Bitcoin wallets, applications, and recorded contracts to create a universal source of truth for all parties assisting in the creation of valuable content in the form of movies. This network can form the foundation on which to build a legal blockchain infrastructure that can eventually facilitate the sale of tokenized securities, discretely disseminate recorded financial data, and transparently distribute revenue to a collective of filmmakers indefinitely.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".