The Lead-Lag Puzzle of Demand and Distribution: A Graphical Method Applied to Movies
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Bibliographic record
Abstract
Understanding the lead-lag relationship between distribution and demand is an important and challenging issue for all marketers. It is particularly challenging in the movie industry, where the very short lifespan and decaying revenue and exhibition patterns of motion pictures means that the associated time series are short and nonstationary, rendering existing econometric methods unreliable. We propose an alternate method that uses state-space diagrams to determine lead-lag relationships. Straightforward to apply and interpret, it takes advantage of the eye’s ability to see patterns that algebra-based formulations cannot easily recognize. A number of validation tests are provided to illustrate the usefulness and limitations of the method. We study the weekly data for 231 major movies released in 2000–2001. While econometric methods do not provide consistent results, the graphical method of visually inferred causality clearly shows a pattern that demand leads distribution for most movies. In other words, the dominant industry pattern is one of movie exhibitors monitoring box office sales and then responding with screen allocation decisions. The managerial implications of these findings are discussed.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it