Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    The algorithmic big Other: using Lacanian theory to rethink control and resistance in platform work
    (2023-01-01) Salter LA; Dutta MJ
    Despite burgeoning literature on platform work, there has been a lack of scholarship which carefully considers what we mean by the terms control and (particularly) resistance in the context of algorithmic management. This article draws on Lacanian psychoanalytic theory to take a step back and interrogate what exactly we mean by these terms in a setting where increasingly the entity being resisted against is an artificially intelligent machine. This approach offers a nuanced way of thinking through the subjective effects of having an algorithm as a boss, and we argue for its benefits and applicability in the age of the algorithmic episteme. Through the key concept of the Algorithmic big Other, we update Lacan’s classic concept to consider what happens when the Other no longer articulates master signifiers through discourse. What we term collective hysterical resistance, aimed at creating spaces for new forms of knowledge and subjectivity, should re-orient towards enlarging the incomputable, the blind spot of the algorithmic episteme.
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    #IamMetiria: A qualitative case study of agonistic welfare policy debates on Twitter
    (2022-08-01) Salter LA
    #IAmMetiria began on Twitter in July 2017, after a speech by New Zealand Green Party co-leader, Metiria Turei, challenging political consensus on welfare policy. Turei confessed she lied to authorities in the 1990s, prompting a flood of supportive posts. Soon after, right-wing oppositional tweets were posted (n = 288) contesting the arguments of Turei and her supporters, and left-wing responses to those arguments (n = 214). Drawing on Mouffe’s dissensual model, this article undertakes a close, qualitative analysis of those 502 tweets, in order to move towards a method for empirically distinguishing between antagonistic and agonistic tweets, identifying the latter as putting forward arguments which can be identified by the researcher and potentially engaged with by ideologically opposed adversaries. The results show a majority of the tweets were agonistic, with implications for the future study of social media policy debates and for the online practices of scholars.