As a form of protest during the Industrial Revolution, Luddites destroyed weaving machinery they believed would usurp their jobs and render time invested in skill acquisition wasted. Their protest was not in disdain or fear of general technological progress so much as it was a reaction to a development directly affecting their livelihoods and an intent to move into a better bargaining position with employers (Conniff, 2011). Modern usage of the term “Luddite” has expanded to encompass general resistance to many technologies, in addition to naming a fallacious line of thinking. The Luddite fallacy refers to the fear of technological unemployment by way of workplace automation, ultimately causing widespread economic damage. In the case of the
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57-72). While the authors classified 47% of all US jobs at risk of being automated within twenty years, it’s important to examine what characteristics these automatable jobs share (Frey & Osborne, 2013, p. 38). When comparing a data entry technician to an elementary school teacher, several stark differences can be observed. Data entry, in general, is a structured and predictable occupation in which incoming work can be processed uniformly. Expectations are unambiguous and generalized to an entire body of work with little repercussion, given the positions repetitive nature. Conversely, elementary school teachers cannot expect all of their student charges to be uniform in this way, and must adapt not only to individuals, but to the overall class dynamic. Further, while there are academic and professional standards teachers must adhere to, the means by which to achieve them are not necessarily standardized, meaning a number of viable and acceptable ways to accomplish professionalism and engender academic achievement exist. Certainly there are times in data entry where receiving unexpected input or having to override protocol for special cases occur, just as there are tasks an elementary school teacher performs that must be accomplished in a specific manner, such as grading certain types of assignments. Regardless, in discussions on workplace automation, it is important to note the issue is less about specific careers and more about tasks. Industrial Revolution era Luddites recognized this in their concern about having wasted time investing in fostering certain skills machines could now perform, and
57-72). While the authors classified 47% of all US jobs at risk of being automated within twenty years, it’s important to examine what characteristics these automatable jobs share (Frey & Osborne, 2013, p. 38). When comparing a data entry technician to an elementary school teacher, several stark differences can be observed. Data entry, in general, is a structured and predictable occupation in which incoming work can be processed uniformly. Expectations are unambiguous and generalized to an entire body of work with little repercussion, given the positions repetitive nature. Conversely, elementary school teachers cannot expect all of their student charges to be uniform in this way, and must adapt not only to individuals, but to the overall class dynamic. Further, while there are academic and professional standards teachers must adhere to, the means by which to achieve them are not necessarily standardized, meaning a number of viable and acceptable ways to accomplish professionalism and engender academic achievement exist. Certainly there are times in data entry where receiving unexpected input or having to override protocol for special cases occur, just as there are tasks an elementary school teacher performs that must be accomplished in a specific manner, such as grading certain types of assignments. Regardless, in discussions on workplace automation, it is important to note the issue is less about specific careers and more about tasks. Industrial Revolution era Luddites recognized this in their concern about having wasted time investing in fostering certain skills machines could now perform, and