Code Nation. Michael J. Halvorson

America Calling: A Social History of the Telephone to 1940 (Berkeley: University of California Press, 1994); and the essay collection Does Technology Drive History? The Dilemma of Technological Determinism, eds. Merritt Roe Smith and Leo Marx (Cambridge, MA: The MIT Press, 1994).

      4.See Thomas P. Hughes, American Genesis: A Century of Invention and Technological Enthusiasm, 1870–1970, Second Edition (Chicago: University of Chicago Press, 2004); David A. Hounshell, From the American System to Mass Production, 1800–1932: The Development of Manufacturing Technology in the United States (Baltimore, MD: The Johns Hopkins University Press, 1984).

      5.Nelson, Computer Lib, 43.

      6.Clive Thompson, Coders: The Making of a New Tribe and the Remaking of the World (New York: Penguin Press, 2019), 11.

      7.Georg Simmel first developed the idea of Cross-cutting social circles “cross-cutting social circles” to discuss how different groups meet at points of common interest, dispute, or compromise. See Georg Simmel, Conflict and The Web of Group-Affiliations, trans. Kurt H. Wolff and Reinhard Bendix, respectively (Glencoe, IL, 1955, original Berlin, 1908). For additional studies in the history of technology that have influenced my approach, see Joseph J. Corn, ed., Imagining Tomorrow: History, Technology, and the American Future (Cambridge, MA: The MIT Press, 1986); David E. Nye, Narratives and Spaces: Technology and the Construction of American Culture (Exeter, UK: University of Exeter Press, 1997); Nina Lerman, Arwen Mohun, and Ruth Oldenziel, “The shoulders we stand on and the view from here: Historiography and directions for research,” Technology and Culture 38 (1997): 9–30; David E. Nye, Consuming Power: A Social History of American Energies (Cambridge, MA: The MIT Press, 1998); Joseph J. Corn, The Winged Gospel: America’s Romance with Aviation (Baltimore, MD: Johns Hopkins University Press, 2002); Greg Downey, “Commentary: The Place of Labor in the History of Information-Technology Revolutions,” in Uncovering Labour in Information Revolutions, 1750–2000 (International Review of Social History), vol. 38 Supplement 11 (2003), 225–261; Lisa Gitelman, Always Already New: Media, History, and the Data of Culture (Cambridge, MA: The MIT Press, 2008); and Christopher Tozzi, For Fun and Profit: A History of the Free and Open Source Software Revolution (Cambridge, MA: The MIT Press, 2017).

      8.Steve Lohr, Go To: The Story of the Math Majors, Bridge Players, Engineers, Chess Wizards, Maverick Scientists, and Iconoclasts—The Programmers Who Created the Software Revolution (New York: Basic Books, 2001), 6–7.

      9.International Data Corporation, 2014 Worldwide Software Developer and ICT-Skilled Worker Estimates (Framingham, MA: International Data Corporation, 2014).

      10.Jane Margolis, Rachel Estrella, Joanna Goode, Jennifer Jellison Holme, and Kimberly Nao, Stuck in the Shallow End: Education, Race, and Computing, Updated Edition (Cambridge, MA: The MIT Press, 2017). See also J. Margolis, J. Goode, and K. Binning, “Exploring computer science: active learning for broadening participation in computing,” Computing Research News 27, no. 9 (October 2015).

      11.Yasmin Kafai and Quinn Burke, Connected Code: Why Children Need to Learn Programming (Cambridge, MA: The MIT Press, 2016).

      12.See “Code.org 2018 Annual Report,” February 12, 2019, 3. https://code.org/files/annual-report-2018.pdf. Accessed August 9, 2019.

      13.For a summary of the current concerns and priorities in the computational literacy field, see Emmanuel Schanzer, Shriram Krishnamurthi, and Kathi Fisler, “Education: what does it mean for a computing curriculum to succeed?” Communications of the ACM 62, no. 5 (2019): 30–32.

      14.Maurice Wilkes, David Wheeler, and Stanley Gill, Preparation of Programs for an Electronic Digital Computer (Reading, MA: Addison-Wesley, 1951).

      15.Joy Lisi Rankin, A People’s History of Computing in the United States (Cambridge, MA: Harvard University Press, 2018), 68–69, 94–100.

      16.An example of this work is Walter Isaacson, The Innovators: How a Group of Hackers, Geniuses, and Geeks Created the Digital Revolution (New York: Simon and Schuster, 2014). An intriguing new approach is Margaret O’Mara’s history of Silicon Valley, which connects the technical and business development of the region to local and national politics. See Margaret O’Mara, The Code: Silicon Valley and the Remaking of America (New York: Penguin Press, 2019).

      17.See James W. Cortada, IBM: The Rise and Fall and Reinvention of a Global Icon (Cambridge, MA: The MIT Press, 2019), especially chapter 14. Cortada was well positioned to write this history because he is a former IBM employee as well as a professional historian.

      2Four Computing Mythologies Computing mythologies

       “Total learning expands when the range of spontaneous learning widens… and both liberty and discipline flower.”

      Ivan Illich, Tools for Conviviality (1973)

       “In recent years, I have talked to a number of top industry researchers and implementors who are reluctant to hire computer science graduates at any level. They prefer to take engineers or mathematicians, even history majors, and teach them programming.”

      David Lorge Parnas, Computer (1990)¹

      When it comes to social movements, the groups that strive toward a common goal with a shared sense of purpose are often the most successful. The Learn-to-program movement learn-to-program movement of the 1970s and 1980s fits this pattern, as do many of the recent computer literacy initiatives, including Code.org’s Hour of Code. According to sociologists, the Ideological beliefs ideological beliefs that ground social movements act as a bulwark for striving organizations, strengthening the commitment of both leaders and members.² Ideological beliefs also help adherents imagine a new world order, and they justify the relatively high levels of personal investment and resources that social movements require. Ideologies set the expectations of a movement’s believers, so that adherents can learn what the group is trying to accomplish and how they should propagate their beliefs. When the going gets tough, ideological commitments keep a social movement going.

      This chapter explores four powerful ideologies that influenced America’s burgeoning computer industry in the 1960s and 1970s, each influencing the learn-to-program movement in its own way. Although Parts II and III of this book narrate how Americans learned to code in the 1980s and 1990s, it was only through an awareness of earlier successes and failures that the microcomputing and personal computing movements took shape. Like other scholars, I choose to use the term Foundation myths foundation myths to describe the ideologies that influenced the computer industry as it emerged from research settings to become a major contributor to the U.S. economy. Foundation myths are socially-constructed memories that can carry important historical and cultural information. They act as social markers, transmitting ideas, beliefs, and worldviews to community members and future generations. Foundation myths summarize historical debates and scientific commitments. They often work subtly, employing the language of metaphor or ritual. In more recent times, computer-related myths are used to celebrate heroic founders and to marginalize illicit behavior. You can often spot these myths when subtle descriptors are used in histories and popular accounts, such as “pioneer,” “entrepreneur,” “evangelist,” “guru,” “hacker,” and “cyberpunk.”

      Among the many possibilities, I have chosen four myths about computer technology and computer programmers to begin this book. I will draw connections between these ideologies and the learn-to-program movement in the chapters that follow. The first mythology is a belief in an ongoing period of crisis in the computer industry related to the complexity of computer systems and the pitfalls of commercial software development. Strongly held beliefs about this “crisis” emerged in the 1960s among software development communities, and it set the expectation that most large software projects would arrive late, over budget, and in poor shape. The second mythology is that the computer industry works best when it is driven by popular, democratic