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Record W4214547508 · doi:10.33178/alpha.22.02

Capturing European crime

2022· article· en· W4214547508 on OpenAlexaboutno aff
Russ Hunter

Bibliographic record

VenueAlphaville Journal of Film and Screen Media · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMovie theaterExhibitionPrestigeFilm festivalFilm studiesOrder (exchange)VisibilityArtArt historyPolitical scienceMedia studiesEconomyHumanitiesGeographySociologyBusinessEconomics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.033
GPT teacher head0.210
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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