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The Age of Intellectual Property?

Lea Shaver

Associate ProfessorLea Shaver taught at Yale Law School and Hofstra Law School before joining the IU McKinney School of Law faculty in 2012. She holds a J.D. From Yale Law School and an M.A. from the University of Chicago. Professor Shaver was a summer clerk to Hon. David F. Hamilton and a Fulbright Scholar in South Africa, where she supported litigation advancing the constitutional rights to housing, education, and water. Her research focuses on intellectual property, innovation, access, and human rights.

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3 Responses

  1. Maryland Conservatarian says:

    Wow – an Ayn Rand reference without snark or a sneer. Nice.

    Yesterday (2/2/11) would have been her 106th birthday.

  2. Frank Pasquale says:

    Very interesting points. Post-Rand, I think there is also a counter-movement in libertarian thought to consider IP as a form of government intervention. See, e.g.,

    “Several writers, Stephan Kinsella most notably among them, have argued that patents and copyrights should not form part of a proper libertarian law code. These writers modify and extend the work of Murray Rothbard, who allowed copyrights but not patents.”

    Dean Baker pushes this viewpoint for the libertarian left.

    I personally have found Tom W. Bell’s conception of copyright as author’s welfare ( and the creep toward “regulatory copyright” rather appealing. I believe that, the more one looks at the details in the copyright code, the more it appears to be a finely grained system of wealth redistribution. (For just two of many examples, consider all the rules on what constitutes a “public performance,” or the rules regarding compensation via the CRB (nee CARP) for compulsorily licensed performances (at Though I will admit that Michael Carrier’s work comparing property and IP has pushed me back toward seeing the appropriateness of the “property” analogy.

  3. A.J. Sutter says:

    This exercise is a wonderful illustration of the fallacy of “information flows”. What do these graphs mean?

    Google Labs’s explanation (see supplemental online material to the Science Express paper) distinguishes three types of data: the number of times an ‘n-gram’ appeared, the number of pages on which it appeared, and the number of books in which it appeared. For example, Chisum’s multi-volume patent treatise in and of itself might skew the results, if the right category isn’t viewed.

    Which type is represented in the percentages in each of the graphs above?

    The current version of the Google corpus doesn’t include part-of-speech tagging (SOM@25), or control for plurals and other grammatical declension and conjugation. So a search for “patent” doesn’t include “patents” (try it) and doesn’t distinguish the use of “patent” as an adjective or verb. Again, this could affect the metric, depending on the category. The Google corpus is also different from other linguistic corpora in that collocations aren’t visible. I.e, the context of the word’s usage can’t be shown. The mere fact that an “n-gram” appears in the historical record on a certain number of occasions doesn’t tell us whether or how its meanings or connotations have changed. This, in turn, ought to make us cautious in our interpretation of the graphs.

    By the way, the paper in Science Express makes clear that the tool relates only to about 1/3 of Google’s “vast repository of digitized books.”

    A prime motivation for this tool seems to be that Google wanted to rush into print with a fancy new word, “culturomics,” which “extends the boundaries of rigorous quantitative inquiry to a wide array of new phenomena spanning the social sciences and the humanities.” No more of that touchy-feely, qualitative stuff. (Sc., All your base are belong to us.)

    Does beguilement by pretty graphs whose meaning isn’t clear, or whose ambiguity is glossed over, really constitute knowledge? (Sorry, awkward question.) If this sort of uncritical usage of the graphs is meant to exemplify “access to knowledge,” it’s not at all encouraging about the referent of that 3-gram.