Bibliographic record
Abstract
This book examines film classification as a practice of regulative social labelling that relies heavily upon culturally constructed boundaries between “types” of films in distinctive national contexts. The analysis draws parallels and distinctions between governmental policies in these contexts, as well as between various social control mechanisms at work within a wide-reaching network of institutions beyond censorial bodies themselves, including news media, film festivals, and advocacy groups. The latter half of the study illustrates the means by (and ends to) which the regulation of film content persists in the “post-censorship” media landscape of Britain, Canada, Australia, and (where this model has most matured) the United States. While the case studies examined each involve distinct problematics, what links them conceptually is attention to how notions of, and related to, film classification, categorization, or labelling manifest in regulatory, artistic, and commercial market contexts: ranging from ratings institutions to journalistic criticism, film distribution, and advertising practices. The study also draws comparison between now obsolete formal censorship practices and machinations of the US “ratings” model of classification that Great Britain, Canada, and Australia moved swiftly toward during the late 1990s and early 2000s.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".