Rogers (2013)

From Copyright EVIDENCE

Advertising Architectural Publishing of books, periodicals and other publishing Programming and broadcasting Computer programming Computer consultancy Creative, arts and entertainment Cultural education Libraries, archives, museums and other cultural activities

Film and motion pictures Sound recording and music publishing Photographic activities PR and communication Software publishing Video game publishing Specialised design Television programmes Translation and interpretation

1. Relationship between protection (subject matter/term/scope) and supply/economic development/growth/welfare 2. Relationship between creative process and protection - what motivates creators (e.g. attribution; control; remuneration; time allocation)? 3. Harmony of interest assumption between authors and publishers (creators and producers/investors) 4. Effects of protection on industry structure (e.g. oligopolies; competition; economics of superstars; business models; technology adoption) 5. Understanding consumption/use (e.g. determinants of unlawful behaviour; user-generated content; social media)

A. Nature and Scope of exclusive rights (hyperlinking/browsing; reproduction right) B. Exceptions (distinguish innovation and public policy purposes; open-ended/closed list; commercial/non-commercial distinction) C. Mass digitisation/orphan works (non-use; extended collective licensing) D. Licensing and Business models (collecting societies; meta data; exchanges/hubs; windowing; crossborder availability) E. Fair remuneration (levies; copyright contracts) F. Enforcement (quantifying infringement; criminal sanctions; intermediary liability; graduated response; litigation and court data; commercial/non-commercial distinction; education and awareness)

Source Details

Rogers (2013)
Title: Substantially Unfair: An Empirical Examination of Copyright Substantial Similarity Analysis among the Federal Circuits
Author(s): Rogers, E.
Year: 2013
Citation: Rogers, E. (2013) Substantially Unfair: An Empirical Examination of Copyright Substantial Similarity Analysis among the Federal Circuits. Mich. St. L. Rev. 893
Link(s): Definitive
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About the Data
Data Description: The study builds on the regression analysis initially conducted by Lippman (2013), examining and coding 230 circuit court substantial similarity cases. 12 variables were used including e.g. subject-matter, court level, methods used to determine similarity (e.g. ordinary observer, total concept and feel etc.).
Data Type: Primary and Secondary data
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Cross Country Study?: No
Comparative Study?: No
Literature review?: No
Government or policy study?: No
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Abstract

“This Note builds upon the regression analysis presented in Lippman's The Beginning of the End. The prior lack of empirical data on the substantial similarity test of copyright infringement law was responsible for inciting legal commentary, which often placed blame on the judiciary for maintain- ing tests that were both impossible to use and unfriendly to all.’ This Note builds upon Lippman's work by assigning "hard numbers" to the results of substantial similarity cases at the appellate level, while determining how selected variables influence case outcomes. This empirical data then will help evaluate the accuracy of the legal commentary.“

Main Results of the Study

The study finds differences in win rates across circuit courts. The 2nd and 9th circuits have substantially better win rates for plaintiffs on appeal than at trial, with reverse being true of 11th. The author this suggests this is due to the 11th circuit explicitly adopting a defined substantial similarity test, and thus having more clarity and precision in their initial ruling; whereas the 2nd and 9th circuits' lack of defined test results in frequent overturning.Circuit courts may be further grouped into preferred substantial similarity tests:• 1st, 2nd, 3rd, 5th, 7th = ordinary observer• 4th 8th and 9th = intrinsic/extrinsic• 6th, 10th, 11th = filtration/comparison testThe test employed has a tangible outcome, with the filtration/comparison statistically less likely to win, and extrinsic/intrinsic more likely to win. Furthermore, so-called “high-tech” (e.g. science, architecture, software) cases are 13% more likely to be successful than low tech (e.g. literature, music, movies) cases.

Policy Implications as Stated By Author

Given the differences in success rates across courts, the author suggests vesting one court with subject-matter jurisdiction in order to promote consistency. Secondly, given the disproportionate likelihood of high-tech cases to succeed, the author suggests that experts should be involved in court testimony (currently restricted), as this may reduce uncertainty and cautious approaches.



Coverage of Study

Coverage of Fundamental Issues
Issue Included within Study
Relationship between protection (subject matter/term/scope) and supply/economic development/growth/welfare
Relationship between creative process and protection - what motivates creators (e.g. attribution; control; remuneration; time allocation)?
Harmony of interest assumption between authors and publishers (creators and producers/investors)
Effects of protection on industry structure (e.g. oligopolies; competition; economics of superstars; business models; technology adoption)
Understanding consumption/use (e.g. determinants of unlawful behaviour; user-generated content; social media)
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Coverage of Evidence Based Policies
Issue Included within Study
Nature and Scope of exclusive rights (hyperlinking/browsing; reproduction right)
Exceptions (distinguish innovation and public policy purposes; open-ended/closed list; commercial/non-commercial distinction)
Mass digitisation/orphan works (non-use; extended collective licensing)
Licensing and Business models (collecting societies; meta data; exchanges/hubs; windowing; crossborder availability)
Fair remuneration (levies; copyright contracts)
Enforcement (quantifying infringement; criminal sanctions; intermediary liability; graduated response; litigation and court data; commercial/non-commercial distinction; education and awareness)
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Datasets

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