Doing Duoethnography: Addressing Essential Methodological Questions
Bibliographic record
Abstract
Duoethnography is a collaborative research methodology that invites researchers to serve as sites of inquiry. Through juxtaposition, the voices of each researcher are made explicit, working in tandem to untangle and disrupt meanings about a particular social phenomenon. We gravitate to duoethnography for its evocative power and the opportunity this methodology provides to engage in meaningful self-study in the presence of another. Yet, we grapple with methodological issues related to the unseen and unspoken enactments of the methodology. This article makes transparent the process of engaging in duoethnography by modeling its polyvocal dialogic nature while simultaneously addressing five essential questions about this collaborative research methodology. In this article, we retrace our collective journey engaging in duoethnography over the past 10 years, reflecting upon how our understanding and engagement with the methodology has shifted and expanded with each new inquiry. We make visible what is often invisible in the process of doing duoethnography, explicitly discussing our process for beginning and concluding a duoethnography, addressing what constitutes duoethnographic data, and the importance of cultivating a trustworthy and safe dialogical space. This article contributes to the existing methodological literature on duoethnography and further substantiates and generates transparency and teachability of this collaborative research approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.349 | 0.407 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.015 | 0.052 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".