Join us for an open conversation on academic writing, its beauty and challenges, and the pressure of “you should be writing” as early career scholars over a coffee on March 14th!
Academic writing can be a fulfilling but also challenging and overwhelming experience for early career scholars. On March 14th from 13.00 to 14.30, please join us for an honest exchange on our struggles and great moments in writing. Daniela Rothe (Writing Center, University of Innsbruck) will talk about the practice of journalling in academia, the tinkering between the scientific object itself and writing, and the re-(production) of gender stereotypes in scientific writing. Monica, Milena, and Ellen (early career researchers at the University of Innsbruck & Leuphana University Lüneburg) will share their writing experiences based on a collective letter diary they wrote to each other over a couple of months to reflect on their writing practices.
We would love to hear about your experiences, too, and together question and discuss the norms and ways of academic writing.
In this article in Organization, we, Milena Leybold and Monica Nadegger, unpack how stigmatized groups reconstruct stigma despite their communicative separation. We conducted a netnographic study to investigate a case of pole dancers—and later sex workers, strippers, and other stigmatized groups—protesting the stigmatization practice of shadowbanning and the sex-work stigma on Instagram.
Although the digital space offers many opportunities in terms of data collection, analysis and new data itself, part 3 of the series ” Thoughts on Digital Qualitative Research” addresses the challenges going hand in hand with digital research. Although each method and each research project may face other or additional questions and difficulties, the challenges presented here are valid for all scholars who aim to conduct digital qualitative inquiry.
The Nature and Quality of Data
The nature and quality of data are crucial for coherent and transparent interpretation. In early research, digital data often required grounding in the offline world to be seen as valid (Rogers, 2013). Although today not all data require grounding outside the digital sphere (Rogers, 2013) as they often describe natively digital phenomena, the methodological context is essential to understand the nature and meaning of the data analysed (Dicks, 2012). Quinton and Reynolds (2018) argue that researches have to define their stance but can freely classify their approaches in terms of methodology.
Part 2 of the series examines the emerging tensions and questions for qualitative research in a Big Data world.
Boyd and Crawford (2012) define the shift to Big Data as a socio-technical phenomenon. The term Big Data describes the huge amount of data aggregated through interaction online and the ability to cross-reference these large data sets for new insights. Discussing the troubling concept of data, Markham (2018) and Rogers (2013) argue that datafication as ideological focus and the methodological turn towards digital research techniques quantifying social processes pose a temptation as well as a challenge for qualitative research at the same time.
The technological ability to collect data does replace the question if qualitative oriented scholars should collect such large amounts of data (Tiidenberg, 2018), from an ethical as well as from a practical point of view (Markham, 2012). Further, these large data sets do not fully represent social phenomena (Markham, 2018), but Big Data changes the way knowledge is created and research is done by changing the instruments, the focus and the process of research (boyd & Crawford, 2012). Markham (2018, p. 520) sees the turn to the quantification in data analysis, the subsumption of qualitative inquiry as an add-on for data-driven science and the pursuit of generalizability as an “ongoing risk, which the interpretative movement has long sought to combat.”
In this short series about qualitative digital methods, I want to introduce basic assumptions, challenges and definitions for qualitative research in the digital space. The first part sheds light on the definition of digital research.
As Hine (2005) and Quinton and Reynolds (2018) state, there is no clear paradigm or restriction for digital research by now. Researchers try to adapt their methodological principles to the digital environment and migrate their ontological and epistemological views into the digital research field.