Dialogic Interdisciplinary Self-Study Through the Practice of Duoethnography
Bibliographic record
Abstract
A number of scholars have begun to use duoethnographies—a dialogic and relational form of research—to examine their own beliefs and perceptions in relation to their curriculum, classes, and professional behavior. Working in tandem with their duoethnography partner, these scholars seek to restory and reconceptualize their perception of these beliefs and of practice. This chapter explores the value of duoethnography to the study of interdisciplinary practice. This value is premised on the view that the “findings” of research are an artifact of its form: dialogic and relational forms of research help to (1) facilitate deeply emic, personal, and situated understandings of practice and (2) promote personal reflexivity and changes in practice. This chapter presents theory underlying duoethnography and, drawing from the subsequent chapters of the book, offers a number of examples of duoethnographies of practice. 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.
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.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".