Literature DB >> 28633611

Rawification and the careful generation of open government data.

Jérôme Denis1, Samuel Goëta2.   

Abstract

Drawing on a two-year ethnographic study within several French administrations involved in open data programs, this article aims to investigate the conditions of the release of government data - the rawness of which open data policies require. This article describes two sets of phenomena. First, far from being taken for granted, open data emerge in administrations through a progressive process that entails uncertain collective inquiries and extraction work. Second, the opening process draws on a series of transformations, as data are modified to satisfy an important criterion of open data policies: the need for both human and technical intelligibility. There are organizational consequences of these two points, which can notably lead to the visibilization or the invisibilization of data labour. Finally, the article invites us to reconsider the apparent contradiction between the process of data release and the existence of raw data. Echoing the vocabulary of one of the interviewees, the multiple operations can be seen as a 'rawification' process by which open government data are carefully generated. Such a notion notably helps to build a relational model of what counts as data and what counts as work.

Entities:  

Keywords:  administration; data labour; invisible work; open data; open government; raw data

Mesh:

Year:  2017        PMID: 28633611     DOI: 10.1177/0306312717712473

Source DB:  PubMed          Journal:  Soc Stud Sci        ISSN: 0306-3127            Impact factor:   3.885


  2 in total

1.  Caring for data: Value creation in a data-intensive research laboratory.

Authors:  Clémence Pinel; Barbara Prainsack; Christopher McKevitt
Journal:  Soc Stud Sci       Date:  2020-02-13       Impact factor: 3.885

2.  Datafication and accountability in public health: Introduction to a special issue.

Authors:  Klaus Hoeyer; Susanne Bauer; Martyn Pickersgill
Journal:  Soc Stud Sci       Date:  2019-08       Impact factor: 3.885

  2 in total

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