Short abstract:

Guided autoethnography trains participants to conduct granular analyses of lived experience of digital transformations. Their findings are aggregated to identify larger patterns. In this paper we present examples of how disparate logics are not blended, but used in tandem for different purposes.

Long abstract:

Autoethnography is a mindset as much as a set of techniques that draws on the strength of subjective ways of knowing, long recognized in non-western contexts, to build deep and granular analyses of cultural meaning emerging from one’s own experiences of situations. Autoethnography embraces contingency, that the ‘truth’ of a situation depends on a variety of factors, it highlights reflexivity as a tool for examining one’s own lens for understanding, and it overtly acknowledges that methods shape what is understood.

This approach may seem to contradict computational analytics, whereby data is used in forms already abstracted from experience, among other differences. However, when considering the inductive goals of pattern recognition, autoethnography and largescale data analytics can be aligned, if not combined.

This contribution describes how a research team builds on Markham’s “Guided Autoethnography” approach to conduct an aggregated autoethnography across four countries. Researchers train young adults to conduct autoethnographies of their own digital lived experience, which produces raw data interwoven with ‘thick description’ ethnographic interpretations. Researchers analyze these, generating emergent themes, which in turn are used to reconfigure some of the large pool of aggregated data into new datasets for larger-scale qualitative or computational cross-country analyses.

We discuss merits, challenges, and possibilities of using seemingly disparate epistemological logics in parallel. To retain their independent strengths, they are used in separate stages: autoethnography provides granular level patterns, while computation explores patterns at scale. The question raised is how to ensure the original ethnographer/participants remain connected to possible largescale interpretive discussions.