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Record W4402148360 · doi:10.47788/yxnw9912

The Screen Censorship Companion

2024· book· en· W4402148360 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Exeter Press eBooks · 2024
Typebook
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsCensorshipArtPolitical scienceLaw

Abstract

fetched live from OpenAlex

Throughout the history of film, censorship has existed everywhere—in all shapes, colours, and dimensions. The act of restricting the free production, circulation, screening, and consumption of movies was never unique to authoritarian regimes. Censorship has had far-reaching implications for filmmakers, distributors, exhibitors, and audiences across generations and across genres, including the self-censorship of audiences disciplined into particular viewership positions. Today, soft and hard censorship coexist in ever-more fluid forms; the banning, regulating, trimming, and tailoring of films for ‘harmless’ consumption all exemplify wider debates about access to media. This companion brings together contemporary and historical views on censorship, covering Argentina, Canada, Chile, Colombia, Denmark, France, Germany, Italy, Japan, Norway, Poland, Sweden, Turkey, the United Kingdom, and the United States. The book considers Hollywood’s practices and the United States’ legislative context as important frames of reference for the study of filmed entertainment censorship, be they concerned with obscene materials or plain mainstream movie fare. American cinema remains a wider compass, as evidenced by how studies in this companion, which deal with local and regional censorship, appear to have American movies as their targets. This volume showcases the broad international scope of censorship through detailed examinations of censorship practices. The diversity of case studies is an indication of the global reach of censorship—nothing can escape its grasp. Ultimately, the censorship of screen access is a struggle for power and control; this book demonstrates how intense this struggle can become, and how compromises and solutions are found.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.070
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0100.007
Open science0.0010.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0700.014

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 head0.065
GPT teacher head0.287
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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
Published2024
Admission routes1
Has abstractyes

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