Community, Identity, and Graduate Education: Using Duoethnography as a Mechanism for Forging Connections in Academia
Bibliographic record
Abstract
This chapter offers a theorization of duoethnography as both a methodology and a pedagogy for self-examination, particularly in the realm of graduate education. Based on a three-month duoethnographic project, Diaz and Grain illustrate the continuous tension between their current student identities and their future scholar identities, and between idealism and cynicism, asking, “how can we navigate these tensions by collaboratively examining and challenging our histories?” Through duoethnography, the authors deepen their perspectives on social justice and privilege, extending the idea that this methodology can also be used as a tool to re-story narratives and find value in the complex and contradictory identities found in the educational process. This chapter highlights how duoethnography can provide learners with opportunities for collaborative development of community, identity, and purpose. 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.030 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".