A book I just became aware of that I am very excited about (thanks to Jessamyn West for posting a screenshot of her ‘summer reading’ on facebook, bringing it to my attention!)
Why the card catalog―a “paper machine” with rearrangeable elements―can be regarded as a precursor of the computer.
Today on almost every desk in every office sits a computer. Eighty years ago, desktops were equipped with a nonelectronic data processing machine: a card file. In Paper Machines, Markus Krajewski traces the evolution of this proto-computer of rearrangeable parts (file cards) that became ubiquitous in offices between the world wars.
The story begins with Konrad Gessner, a sixteenth-century Swiss polymath who described a new method of processing data: to cut up a sheet of handwritten notes into slips of paper, with one fact or topic per slip, and arrange as desired. In the late eighteenth century, the card catalog became the librarian’s answer to the threat of information overload. Then, at the turn of the twentieth century, business adopted the technology of the card catalog as a bookkeeping tool. Krajewski explores this conceptual development and casts the card file as a “universal paper machine” that accomplishes the basic operations of Turing’s universal discrete machine: storing, processing, and transferring data. In telling his story, Krajewski takes the reader on a number of illuminating detours, telling us, for example, that the card catalog and the numbered street address emerged at the same time in the same city (Vienna), and that Harvard University’s home-grown cataloging system grew out of a librarian’s laziness; and that Melvil Dewey (originator of the Dewey Decimal System) helped bring about the technology transfer of card files to business.
I haven’t read it yet myself.
But I’ve thought for a while about how card catalogs were pre-computer information processing systems (with some nostalgia-for-a-time-i-didn’t-experience-myself of when library science was at the forefront of practically-focused information processing system theory and practice).
And I’ve realized for a while that most of our legacy data was designed for these pre-computer information processing systems. And by “legacy” data, I mean the bulk of data we have :) MARC, AACR2, LCSH, even call number systems like DDC or LCC.
If you want to understand this data, you have to understand the systems it was designed for — their affordances and constraints, how they evolved over time — and thinking of them as information processing machines is the best way to understand it, and understand how to make use of it in the present digital environment, or how to change it to get the most benefit from the different constraints and affordances of a computerized environment.
So I can’t quite recommend the book, cause I haven’t read it myself yet — but I recommend it anyway. :)