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"Oded Shenkar, Professor of Business at Ohio State University, has written widely on the Chinese economy, notably in his acclaimed book "The Chinese Century" (2004). He has argued that China has been one of the main drivers and a primary beneficiary of the emergence of a 'copycat economy', where imitators increasingly win over innovators in capturing economic value. It's a subject he explores further in his new book, "Copycats: How Smart Companies Use Imitation to Gain Strategic Edge" (Harvard Business Press). In this talk, he will discuss whether Chinese businesses will, in the coming years, succeed in adding innovation to their repertoire, and whether this could result in a hybrid imitation/ innovation formula which will enable them to trump the competitive advantage of the world's major multinationals - or whether these multinationals will respond by learning the art of imitation themselves. Edwin Chan of the Harvard Design School and Tony Chen of Jones Day in Shanghai will also join in the debate on innovation and imitation in China. Moderated by Russell Flannery, Shanghai Bureau Chief of Forbes magazine."

"While governments throughout the world have different approaches to how they make their public sector information (PSI) available and the terms under which the information may be reused, there appears to be a broad recognition of the importance of digital networks and PSI to the economy and to society. However, despite the huge investments in PSI and the even larger estimated effects, surprisingly little is known about the costs and benefits of different information policies on the information society and the knowledge economy. By understanding the strengths and weaknesses of the current assessment methods and their underlying criteria, it should be possible to improve and apply such tools to help rationalize the policies and to clarify the role of the internet in disseminating PSI. This in turn can help promote the efficiency and effectiveness of PSI investments and management, and to improve their downstream economic and social results. The workshop that is summarized in this volume was intended to review the state of the art in assessment methods and to improve the understanding of what is known and what needs to be known about the effects of PSI activities."

"Open source software developers have a powerful set of tools to make distributed authoring of software possible: diff to identify what's changed, patch to apply those changes elsewhere, version control to track changes over time and show provenance. Patch management would be just as important in a collaborative open data project, where users and other researchers might be submitting new or revised data. What would git for data look like? Heck, what would a local branch look like? I have a new attribute, you have a different projection, she has new rows, how does this all tie back together? (I eagerly await claims that RDF will solve this problem and all others) That's just development. The interface between developers and users is the release. State of the art for a lot of government data is the equivalent of source.tar.gz. No version numbers, much the ability to download older versions of the datasets or separate stable and development branches. Why would we want to download the historic version of a dataset? Because a paper used it and we want to test the analysis software that the paper used to ensure we get the same answer. Or because we want to see what our analysis technique would have shown with the knowledge that was available back then. Or simply to be able to track defects. The users of data will have to adapt to the idea of versions, like the users of software have. The maintainers of the dataset might release five different versions of it while you're writing your analysis code, so it can't be a painful process to incorporate the revised data into your project. With software we have shared libraries and dynamic libraries, supported by autotools and such packages. Our code has interfaces and a branch that promises backwards compatibility. What would that look like for data? And what is the data version of the dependency hell that software developers know all-too-well (M 1.5 depends on N 1.7 and P 2.0, but P 2.0 requires N 2.0, and upgrading N to 2.0 br

The Barbie parody case, which was a) thrown out as being frivilous, with b) an attorney's fee order of millions!

Very reminiscent of the debates around cultural appropriation of the Alberto Korda photo of Che.