Abstract"Critical geographers" concerned with cartography insist maps are first and foremost social artefacts that must be "read" as authorial documents rather than perceived as data statements. Their argument is typically dismissed as trivial because the relation between intent and output has rarely been demonstrated in a critical way. This article seeks to demonstrate the degree to which authorial intent defines map content and appearance through an analysis of a single set of maps. All are based on the original data collected in 1854 by Dr John Snow as part of his study of the cholera outbreak in London's Soho district. Snow's original map is included as baseline for a study that includes versions of the Snow map by a range of authors, including Cliff and Haggett; the US Centers for Disease Control; Gilbert; Tufte; and Monmonier. The resulting appropriations bear progressively less resemblance to the original work, despite the use of the same data set and its clear availability. The result is a cautionary tale of the distance between maps and the data they represent. The article also insists upon the close relation behveen authorial intent and mapped result irrespective of the data available. Finally, the article concludes that because mapping is not value-free, dangers occur when professional cartographers and geographers attempt to map data from fields in which they are ignorant.Keywords: cartography, cholera, epidemiology, GIS, medical mapping, John Snow
ResumeLes << gkographes critiques ,, qui s'intiressent a la cartographie soutiennent que les cartes sont d'abord et avant tout des artefacts sociaux que I'on doit N lire comme des documents Cmanant d'auteurs plut6t que percevoir com-
Transhumanists advance a "posthuman" condition in which technological and genetic enhancements will transform humankind. They are joined in this goal by bioethicists arguing for genetic selection as a means of "enhancing evolution," improving if not also the species then at least the potential lives of future individuals. The argument of both, this paper argues, is a new riff on the old eugenics tune. As ever, it is done in the name of science and its presumed knowledge base. As ever, the result is destructive rather than instructive, bad faith promoted as high ideal. The paper concludes with the argument that species advancement is possible but in a manner thoroughly distinct from that advanced by either of these groups.
Organizational change in education, as manifested by school reform, is indeed complex. In this article, we describe our experience with organizational change and analyse it using organizational change theories common in education. An evaluation of the reform initiative yielded unexpected problems related to sustainability. As a result, this article revisits the assumptions from the organizational change literature that guided our change strategy. This reflection led to the discovery of key theoretical ideas that, although less familiar, may have more practical utility for those engaged in organizational change within schools.
On January 22, 2020, Johns Hopkins University launched its online COVID-19 dashboard to track in real time what began in December as the regional outbreak of a novel coronavirus first identified in Wuhan, China. The dashboard and its format were quickly adopted by other organizations, making global, national, and regional data on the pandemic available to all. The wealth of data freely offered in this way was collected by syndromic programs whose precise algorithms search official and popular sources for data on COVID-19 and other diseases. The dashboard signals a new phase in the maturation of the ''digital revolution'' from paper resources and, in their popular employ, a ''democratizion'' of data and their presentation. This perspective thus uses the COVID-19 experience as an example of the effect of this digital revolution on both expert and popular audiences. Understanding it permits a broader perspective on not simply the pandemic but also the cultural and socioeconomic context in which it has occurred.Mainstream: Data science output is well understood and (nearly) universally adopted ll
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