Exploring Duoethnography in Graduate Research Courses
Bibliographic record
Abstract
In this chapter we explore the potential of duoethnography as a research methodology, attending to its dialogic and pedagogic features suitable for graduate research courses. Reflecting on our experiences with the approach, the invited co-authors—a professor and his current or former doctoral and graduate students—share insights on duoethnography as particularly salient in teaching collaborative and participatory research methods at graduate and doctoral levels. Through illustrations, we describe this dialogic approach as encouraging self-reflection, and opening up a critical examination of the beliefs and values underlying their practice. Here, we present duoethnography as a democratizing way of resisting some of the dehumanizing neoliberal features of contemporary universities, with encouragement for more scholars to engage and extend duoethnography with students in their university classes. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".