Interviewing the Digital Materialities of Posthuman Inquiry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Have you considered how the many things assisting you with your research—digital recorders, computer assisted qualitative data analysis software (CAQDAS) or even Google Scholar—may also be silently shaping scholarly practices? In this paper, we interrogate the networked, digital landscape of everyday qualitative research practices by unraveling several examples taken from recent empirical studies in educational and social science. Our disentangling and decoding of the digital materialities of qualitative inquiry involves “interviewing” several digital objects—a recording device, a digital camera, an iPod, and a software program—that were recruited at different stages of several contemporary research projects. We deploy Adams and Thompson’s (2011) heuristics for interviewing nonhuman or “thingly” research participants, and apply these to the digital things of qualitative research practices. We suggest that these digital entities—“coded materialities” —participate as co-researchers that transform, extend and support but also deform, disrupt and circumscribe research practice and knowledge construction, and inevitably introduce new tensions and contradictions. Counterpointing two approaches to describing our enacted and pre-objective material worlds—Actor Network Theory and phenomenology, we usher into view some of the hidden and coded materialities of research practice, and glimpse unexpected realities enacted. Such immersive entanglements ultimately raise new questions about the posthumanist fluencies demanded in social science research practice. One such fluency is reckoning with how our agency as researchers is increasing shared, distributed and supported by digital technologies. Our entanglements with coded materialities introduce new ethical tensions and responsibilities into research practice. Second, new fluencies may also be called into play as the researcher’s work is subject to both deskilling and up-skilling as various technologies sit alongside researchers as co-researchers. Third, when data is viewed as lively, relational and mobile, new enactments of data are possible. Learning to work with these complex data circulations is another posthuman research digital fluency. Fourth, the scale, mobility, and spatial arrangements of the research process are being radically reconfigured as increasingly public and fragmented; these new arrangements bring both tensions and opportunities to be. Finally, with data being frozen and thawed in the fluidity of digitized research spaces, researchers must be attentive to how and what data is being included and excluded. We conclude by suggesting that researchers “build in” opportunities to regularly query the digital tools of their trade.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it