Curious case of Rotten Tomatoes : Effects of quality signalling in the US domestic motion picture market.
Bibliographic record
Abstract
Quality signalling in motion picture markets is hardly a new topic. It has been covered by many researchers over the years. However, most of the previous studies focused on quality signals in interactions between moviemakers and moviegoers. This study employs a more holistic approach as the author attempts to evaluate effects of quality signals throughout different stages of movies’ life cycle. The author has identified three audiences that movies are presented to; and, each group of audience generates a quality signal for the next audience. Based on the feedback from test audiences, moviemakers decide on when to show movies to professional critics and when to allow them to publish their reviews. Interpretation of these timelines become quality signals for the professional critics who interpret shorter time slot for review publication as a signal of the low quality of the movie and vice versa. Professional critics write their reviews which when published on review aggregators become quality signals for the moviegoers. Reviews generated by the initial moviegoers are interpreted by the moviegoers who intend to watch movies at a later stage. All three assumptions are operationalised and evaluated in a series of linear regression tests in this research on a sample containing 130 out of 134 widely released movies in the US and Canada domestic market in 2017. All of the abovementioned quality signals found to be significant as they could explain at least 40 % of the variance of respective response variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".