The purpose of this article is to provide insight in standardization education by presenting the results of an international workshop organized by the International Committee for Education about Standardization (ICES) together with findings from literature. The main topics are: needs for standardization education, audiences and learning objectives, contents of an academic curriculum, and available materials for academic teaching. We found an enormous gap between manifest and latent needs for standardization education. The lesson to be learnt from some Asian countries is that this gap can be bridged. First, by a strong national policy which may be part of a regional policy. Secondly, by cooperation between government, industry, national standards body, academia and other educational institutions. The increasing number of initiatives and activities of the last three years indicates that there is a momentum for education on standardization. Our paper provides a structured approach for using this momentum to further develop and implement standardization education. It challenges researchers in the field to interrelate research and education.

This article addresses the problem of entrenchment in large technical systems. It explores in what manner standardization could be used as a means to inscribe flexibility into infrastructures and focuses in particular on the role of standardized gateway technologies. Two cases are examined: the Extensible Markup Language (XML) for structured information exchange and the intermodal freight container, also known as the ISO container.The cases indicate that flexibility is a transient characteristic of gateway standards. Where standards do not meet the needs of subsystems and changed circumstances, competing gateways emerge. Flexibility, entrenchment and competition are part of a cyclic movement in gateway evolution. Entrenchment, Standards and System Flexibility 1Policy developers in the fields of transportation and information networks face a problem that besets most infrastructures and other large technical systems (LTSs): such systems often seem impervious to change. Indeed, most studies on the matter confirm that change is most difficult. The countless number of interdependent socio-technical components and subsystems define the complexity of LTSs. They comprise technical artefacts as well as, for example, the institutional and regulatory contexts of artefact use and production (Kaijser, 1999). Organisations and companies emerge that develop and sustain the system. Each specialises in certain tasks, develops technical add-ons and complementary products, gains experience with (part of) the system, etc. As the infrastructure develops, the number of and interdependence between actors and artefacts grows. Over time, these interdependencies crystallise, solidify, and make manifest a process of Tineke Egyedi, Ph.D, is a co-guest editor of this issue.You can read more about her in the Foreword.

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Most large Information and Communication Technology (ICT) systems develop in a piece-meal fashion. Their complexity and evolution is difficult to manage. They lack flexibility. This contrasts sharply with system design in the batch-wise processing industry, where flexibility has always had a high priority. In this industry, the S88 standard plays an important flexibilityenhancing role. The paper compares the two fields of technology and explores which standards' characteristics increase system flexibility. It examines whether flexibility objectives in both fields differ, and what constitutes a 'flexible standard'. Four standards' characteristics turn out to be important: degree of specificity, level of abstraction, system level, and degree of simplicity. They seem to be a necessary condition for standards to create flexible systems, but whether they are a sufficient condition cannot yet be said.

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1The paper argues that a new category of infrastructures is emerging, user-driven, selforganizing and with de-centralized control: Inverse Infrastructures (IIs) coordination mechanisms). Theoretical concepts are drawn from standardization theory, from studies on Open Source Software communities, and from theories of self-organizing systems (i.e. Complex Adaptive Systems and System-ofSystems theory). The two clusters of II cases are peer-to-peer networks (e.g. Napster, Gnutella and Joost) and wireless networks (Wireless Leiden and FON). The paper concludes that, similar to the behavior of ant colonies, II emergence can be understood as an accumulation of local attempts to optimize a situation. Complex citizen and citizen-company partnerships evolve which compete with existing infrastructure provisions and touch on public values (e.g. privacy, copyright). A policy response is needed.here is a widespread fascination among researchers of various disciplines with order that stems from chaos, with method in madness, with complexity that results from simplicity (e.g. ant colony behavior and Internet; Holland, 1995), and with the coherent, highly valued achievements that stem from self-organization 2 (Open Source Software; Perens, 1999). These examples fascinate us foremost because the outcome of self-organization would lead us to suspect that a large amount of centralized orchestration takes place, whereas the opposite seems to be the case.In the field of ICT infrastructures, a similar phenomenon is occurring. Where previously the grand design, a top-down and centralized approach, seemed imperative for harmonized infrastructure development, at present a different phenomenon is gaining ground: Inverse Infrastructures (Vree, 2003). Inverse Infrastructures (IIs) develop bottom-up, and are driven by users. Self-organization is a key element in their emergence.In the following we will argue that IIs represent a paradigm shift in infrastructure development, a shift which policy makers have difficulties to deal with. Moreover, policy makers have little knowledge and experience 1 We sincerely thank our two colleagues of the ICT section Jan van den Berg en Semir Daskapan for the interesting discussions and feedback they have given us, and the anonymous reviewers of SIIT2007 for their good comments. This work was supported in part by the Next Generation Infrastructures foundation (www.nginfra.nl). 2 Self-organization refers to 'unsupervised learning'. In the case of networks this means that there are inputs and outputs but "no feedback from the environment to say what those outputs should be or whether they are correct. The network must discover for itself patterns, features, regularities, correlations or categories in the input data and code for them in the output. The units and connections must thus display some degree of self-organization" (Hertz et al., 1991, p.197) T