Government versus Industry Self-Regulation: Film Classification in Canada and the United States
Bibliographic record
Abstract
Film censorship in Canada and the United States was similar in the early decades of film, but while the United States moved from state government censorship to industry self-regulated censorship, censorship remains a provincial government responsibility in Canada. In both countries, agencies officially moved from censorship to age classification, but censorship continues. Comparing the histories of classification in both countries shows similar events happened at different times, which may predict industry self-regulated classification coming to Canada. Comparisons of the film classifications issued by six provincial agencies and the film industry in the United States show that Canadian agencies agree on the classification for more than 70% of films, and that Canadian classifications are more liberal than the classifications issued by the American film industry. The findings are consistent with previous studies. The international comparison finding may reflect Canadian liberalism but is more likely the result of the different agency structures. In a democracy, film classification run by the government is responsive and independent. Film classification run by the film industry may be affected by a desire to protect the industry from government classification. The similar classifications among the provinces suggest regional differences are minor. This and other factors lead to the possibility of Canadian provincial film classifications being replaced by the more conservative American industry film classifications.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".