Bibliographic record
Abstract
Contents: M.E. Price, Introduction. Part I:Adopting the V-Chip System: Canada and the U.S. A. MacKay, In Search of Reasonable Solutions: The Canadian Experience With Television Ratings and the V-Chip. S.D. McDowell, C. Maitland, Developing Television Ratings in Canada and the United States: The Perils and Promises of Self-Regulation. M. Heins, Three Questions About Television Ratings. J.M. Balkin, Media Filters and the V-Chip. Part II:Other Perspectives, Other Media. A.M. Hargrave, The V-Chip and Television Ratings: British and European Perspectives. J.T. Federman, Media Ratings Systems: A Comparative Review. J.T. Hamilton, Who Will Rate the Ratings? D.F. Roberts, Media Content Labeling Systems: Informational Advisories or Judgmental Restrictions? C.D. Martin, An Alternative to Government Regulation and Censorship: Content Advisory Systems for Interactive Media. R.M. Mosk, Motion Picture Ratings in the United States. Part III:The Internet Debate. D.J. Weitzner, Yelling Filter on the Crowded Net: The Implications of User Control Technologies. J. Weinberg, Rating the Net. Part IV:Appendix. Canada. United States. Europe. M. Gebauer, B. Sherman, Bibliography.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.069 | 0.027 |
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".