Bibliographic record
Abstract
As “shop windows” for newly produced but not-yet-released films, the study of festivals is one measure by which it is possible to assess the nature, presence and relative quantity of European crime films made in any given year. This article explores the presence of European crime films at European film festivals by examining the complete festival programmes from Cannes, Berlin and Venice. These three festivals are widely regarded as the most prestigious and largest in Europe (forming part of the global “Big 5” alongside Sundance and Toronto) and as such are premier destination for films of all types, particularly European productions. The prestige and visibility afforded by them means that they are key sites of exhibition, marketing and potential distribution to any film that is programmed there. The aim is to begin substantiating the numerical, national and transnational make-up of European crime cinema by using its presence on the European festival circuit as means to highlight its contemporary production status. A systematic analysis of all films programmed at Cannes, Venice and Berlin in order to identify the European crime films present identified a total 289 films (8.05% of the total films programmed at these festivals) out of 3587 films surveyed across a five-year period (2016–2020).
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".