Bibliographic record
Abstract
The MeCETES Film Database is a MS Excel spreadsheet containing data on over 23,000 film titles, released in Europe between 1996 and 2014. Data for each film title includes: - Title - Production year - Production country(s) - Theatrical admissions in 36 European territories, plus US and Canada - Release date - Runtime - Genre - Director(s) - Writer(s) - Actor(s) - Language(s) - Location(s) of filming - Awards/nominations - Key awards (e.g. Oscars, ETA, Berlin) - Metacritic score - IMDb User Rating - IMDb Votes - Tomatometer score - Tomatometer User Rating - Tomatometer Reviews - DVD release date - Budget - Plot - MEDIA distribution support - Eurimages support The MeCETES Film Database was created by Huw D Jones using raw data from various sources, including the European Audiovisual Observatory LUMIERE Pro database, the Internet Movie Database (IMDb), Rotten Tomatoes, Wikipedia, MEDIA and Eurimages. Data from the LUMIERE Pro and IMDb was initially collected on 18-19 November 2015, with further additions and amendments made since that date. Due to the licensing restrictions on the use of admissions data obtained from LUMIERE Pro, the MeCETES Film Database can only be accessed by members of the MeCETES research team. Closed access
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.094 | 0.128 |
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".