How Complex Do Movie Channel Contracts Need to Be?
Bibliographic record
Abstract
The motion picture industry is characterized by a dynamic market environment, limited shelf space and product category management, and consequently, complex channel contracts specifying the split of box office revenue between distributors and exhibitors. Although such a contracting practice creates a considerable administrative effort and channel conflict, it is not clear whether such complexity is necessary for superior channel performance. This study investigates this question by analyzing the impact of movie contract structure on movie scheduling and channel member profitability. We develop and analyze a game-theoretic model using the genetic algorithm approach and a decision support system, SilverScreener, to capture strategic behaviors of channel members in a complex market environment. We find that simpler two-part tariff or 50/50 split contracts perform as well as the current contracts. Thus, the complexity of the market environment need not be reflected in the complexity of the channel contracts. Channel contract structure has significant impact on channel member profitability and the exhibitor's movie-scheduling behavior. In particular, our results indicate that the flat rate contract structure represents an attractive alternative to the current practice for distributors.
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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 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".