Film Classification in Canada and the United States: The Freedom of Government Control?
Bibliographic record
Abstract
The American film industry voluntarily classifies films, using the well-known MPAA ratings.The industry claims this is preferable and more liberal than the alternative approach of government classification.In Canada, film classification is mandatory in most jurisdictions, and performed by provincial governments.This thesis demonstrates that the Canadian government controlled mandatory ratings systems for films results in more liberal film ratings than the voluntary system in the United States, for most mainstream films.An analysis of one hundred recent releases and case studies support this conclusion.The histories and operations of the agencies in Canada and the United States, and differences among the provinces, are reviewed to identify factors leading to the different ratings, as well as challenges to the ratings systems.A key factor may be that Canadian agencies classify films not subject to MPAA classification.28 Rick Lyman, "Hollywood Balks at High-Tech Sanitizers; Some Video Customers Want Tamer Films, and Entrepreneurs Rush to Comply," The New York Times, September 19, 2002, sec.Movies, http://www.nytimes.com/2002/09/19/movies/hollywood-balks-high-tech-sanitizers-some-video-customers-want-tamer-films.html.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".