The hierarchy-of-effects model and prelaunch forecasting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pre-release forecasting of the opening box office revenue allows a studio to prepare a more effective marketing campaign and budget allocation. The purpose of this study is to forecast the opening box office revenue using attitudinal tracking measures. The proposed model aims to establish a relationship between the opening box office and the tracking data of the hierarchy-of-effects (HOE) constructs, which managers can use as the target of marketing planning. To test the causal relationships between the HOE constructs and opening box office revenue, we estimate a serial mediation model that incorporates direct and indirect effects of advertising to the HOE constructs with the covariates including marketing efforts, movie characteristics, and viewer demographics. Based on the posterior predictive distributions of the model parameters, we obtain the forecasts of opening box office revenue as new data become available. The validation results show highly encouraging predictive accuracy, indicating the benefit of utilizing attitudinal tracking.
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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 | 0.005 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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