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A Midsummer Night's Dream, one of the greatest stories ever told . . . in texts?!
This book offers a critical engagement with contemporary IR textbooks via a novel folklorist approach. Two parts of the folklorist approach are developed, addressing story structures via resemblances to two fairy tales, and engaging with the role of authors via framing gestures. The book not only looks at how the idea of 'social science' may persist in textbooks as many assumptions about what it means to study IR, but also at how these assumptions are written into the defining stories textbooks tell and the possibilities for (re)negotiating these stories and the boundaries of the discipline.
This book will specifically engage with how the stories in textbooks constrain how it is possible to define IR through its (re)production as a social science discipline. In the first part, story structures are explored via Donkeyskin and Bluebeard stories which the book argues resemble some structures in textbooks that define how it is permissible to tell stories about IR. In the second part the role of authors is explored via their framing gestures within a text, drawing on a number of fairy tales. By approaching the stories in textbooks alongside fairy tales, Starnes reflects back onto IR the disciplining practices in the stories textbooks tell by rendering them unfamiliar.
Aiming to spark a critical conversation about the role of textbooks in defining the boundaries of what counts as IR and by extension the boundaries of the IR canon, this book is of great interest to students and scholars of international relations.
Exploiting the old maxim that "a picture is worth a thousand words," scientific visualization may be defined as the transformation of numerical scientific data into informative graphical displays. It introduces a nonverbal model into subdisciplines that hitherto employed mostly or only mathematical or verbal-conceptual models. The focus of this monograph is on how scientific visualization can help revolutionize the manner in which the tendencies for (dis)similar numerical values to cluster together in location on a map are explored and analyzed, affording spatial data analyses that are better understood, presented, and used. In doing so, the concept known as spatial autocorrelation - which characterizes these tendencies and is one of the key features of georeferenced data, or data tagged to the earth's surface - is further de-mystified. This self-correlation arises from relative locations in geographic space.
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