Assessing the Quality of Illegal Copies and its Impact on Revenues and Distribution
Bibliographic record
Abstract
Conventional wisdom holds that illegal copies cannibalize legitimate sales, even though previous research has found mixed effects, with illegal copies acting as both a substitute and complement.Yet, a relatively unexamined aspect to date is the quality of illegal copies.Building on product uncertainty and production quality, we propose that higher quality copies can benefit sales when product uncertainty is high, such as during the launch period.Using motion picture and online piracy data, we estimate piracy quality using a latent item response theory (IRT) model based on keyword signals in the copies.An interdependent system jointly estimates movie screens, revenues, downloads, and available illegal copies with piracy quality in both the launch and postlaunch periods.We find that at launch, when rather little is known about the movie, higher quality illegal copies demonstrate a positive effect on revenues (sampling).In the post-launch period, however, higher quality illegal copies exhibit a negative effect on revenues (substitution).The findings suggest producers can alleviate product uncertainty through higher quality samples at product launch while diluting piracy quality post-launch.
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".