  In this workshop paper, we use an empirical example from our ongoing
fieldwork, to showcase the complexity and situatedness of the process of making
sense of algorithmic results; i.e. how to evaluate, validate, and contextualize
algorithmic outputs. So far, in our research work, we have focused on such
sense-making processes in data analytic learning environments such as
classrooms and training workshops. Multiple moments in our fieldwork suggest
that meaning, in data analytics, is constructed through an iterative and
reflexive dialogue between data, code, assumptions, prior knowledge, and
algorithmic results. A data analytic result is nothing short of a
sociotechnical accomplishment - one in which it is extremely difficult, if not
at times impossible, to clearly distinguish between 'human' and 'technical'
forms of data analytic work. We conclude this paper with a set of questions
that we would like to explore further in this workshop.
