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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".