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Record W7033484725

The Progress & Freedom Foundation 051807 - PFF Congressional Seminar on The Complexities of Regulating TV Violence

2007· other· en· W7033484725 on OpenAlexaboutno aff

Bibliographic record

VenueBulletin of Miscellaneous Information (Royal Gardens Kew) · 2007
Typeother
Languageen
FieldComputer Science
TopicHand Gesture Recognition Systems
Canadian institutionsnot available
Fundersnot available
KeywordsCommissionAgency (philosophy)Government (linguistics)Foundation (evidence)WrightRoyal Commission
DOInot available

Abstract

fetched live from OpenAlex

A PFF Congressional Seminar May 18, 2007 12:00 p.m. to 2:00 p.m. Rayburn House Office Building, Room B354 Washington, DC 20515 Speakers: Adam Thierer (Moderator), Senior Fellow, The Progress & Freedom Foundation Robin Bronk, Executive Director, The Creative Coalition Robert Corn-Revere, Partner, Davis Wright Tremaine LLP Jonathan L. Freedman, Professor of Psychology, University of Toronto and Author, Media Violence and its Effect on Aggression Henry Geller, Former General Counsel, Federal Communications Commission On April 25th, the Federal Communications Commission released its long-awaited report on Violent Television Programming and Its Impact on Children. The agency recommended that government assume a greater role in regulating violent video content that comes into the home. The agency concluded that such regulation would "serve the governmentâs interests in protecting the well-being of children and facilitating parental supervision and would be reasonably likely to be upheld as constitutional." Is such regulation necessary? Do parents have the tools at the disposal already to handle this responsibility on their own, or is additional government action needed? How would "excessively violent" content be defined by regulators? Would such rules withstand constitutional scrutiny? This PFF policy seminar will explore the complexities of defining and regulating violent television programming.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.019
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0220.003

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.

Opus teacher head0.015
GPT teacher head0.224
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

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