Rotten Tomatoes takes steps to diversify critics pool
Bibliographic record
Abstract
Film review aggregator Rotten Tomatoes is attempting to diversify its film critic pool after a recent study found the siteâs reviews of top-grossing movies were heavily male-dominated. Rotten Tomatoes wants women.The film critic siteâs reviews of top-grossing movies are heavily male-dominated and they want more female critics to even it out a bit.The company is expanding criteria to be a âTomatometer-approved criticâ and giving $100,000 to non-profit organizations to help pay for attending film festivals.The first $25,000 is going toward sending critics to the Toronto International Film Festival in September.An Annenberg Inclusion Initiative survey of Rotten Tomatoes reviews for last yearâs top box-office hits found that nearly 80 percent of critics were delivered by men.Rotten Tomatoes will now look at an individualâs body of work, including on new media platforms, instead of a criticâs outlet or employer.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.013 |
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; both teacher heads agree on what is shown here.
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".