Modelling heavy tails and skewness in film returns
Why this work is in the frame
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Bibliographic record
Abstract
The average of box-office revenue is dominated by extreme outcomes, with most films earning little and most revenues flowing to a few blockbusters. In this paper the skewness and heavy tails of film returns are formally modelled using skew-Normal and skew-t distributions. Logarithmic skew-Normal and skew-t models of the distribution of box-office revenue are fitted conditional on star actors and directors, budget, release pattern, genre, rating, and year of release. The estimates show significantly more skewness and heavier tails than the log-Normal distribution. It is also found that a wide theatrical release has a much smaller impact on box-office revenue when heavy tails and skewness are explicitly modelled.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it