The History Manifesto by Jo Guldi & David Armitage

Author:Jo Guldi & David Armitage [Guldi, Jo]
Language: eng
Format: epub
Publisher: Cambridge University Press
Published: 2014-10-06T00:00:00+00:00

Chapter 4 Big questions, big data

One of the reasons that a society discovers itself in a crisis of long-term thinking is the problem of information overload. Information overload is not a new story in and of itself. European humanists in the Renaissance experienced it, as new editions of classical texts, new histories and chronology, and new information about the botany and fauna of Asia and the Americas rapidly swamped the abilities of scholars to aggregate information into encompassing theories or useful schedules. Indeed, many of our basic tools for search and retrieval – the index, the encyclopaedia, and the bibliography – came from the first era of information overload, when societies were feeling overwhelmed about their abilities to synthesise the past and peer into the future.1

We live in a new era of ‘big data’, from the decoding of the human genome to the billions of words of official reports annually churned out by government offices. In the social sciences and humanities big data have come to stand in for the aspiration of sociologists and historians to continued relevance, as our calculations open new possibilities for solving old questions and posing new ones.2 Big data tend to drive the social sciences towards larger and larger problems, which in history are largely those of world events and institutional development over longer and longer periods of time. Projects about the long history of climate change, the consequences of the slave trade, or the varieties and fates of western property law make use of computational techniques, in ways that simultaneously pioneer new frontiers of data manipulation and make historical questions relevant to modern concerns.3

Over the last decade, the emergence of the digital humanities as a field has meant that a range of tools are within the grasp of anyone, scholar or citizen, who wants to try their hand at making sense of long stretches of time. Topic-modelling software can machine-read through millions of government or scientific reports and give back some basic facts about how our interests and ideas have changed over decades and centuries. Compellingly, many of these tools have the power of reducing to a small visualisation an archive of data otherwise too big to read. In our own time, many analysts are beginning to realise that in order to hold persuasive power, they need to condense big data in such a way that they can circulate among readers as a concise story that is easy to tell.

While humanity has experimented with drawing timelines for centuries, reducing the big picture to a visualisation is made newly possible by the increasing availability of big data.4 That in turn raises the pressing questions of whether we go long or short with that data. There are places in the historical record where that decision – to look at a wider context or not – makes all the difference in the world. The need to frame questions more and more broadly determines which data we use and how we manipulate it, a challenge that much longue-durée work has yet to take up.

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