Duoethnography and English for research publication purposes: Promises and challenges
Bibliographic record
Abstract
In this paper, we explain how to conduct and apply duoethnography innovatively, given its lack of prescribed methodological steps. We discuss the theoretical underpinnings of duoethnography, its central tenets, its methodological constraints, and its challenges and limitations. More specifically, we explain how this promising innovative methodology can be adopted in the fast-growing field of English for Research Publication Purposes (ERPP) within Applied Linguistics to examine and investigate rhetorical, socio-political, and contextual aspects of the production and dissemination of knowledge and writing for scholarly publication practices. This paper aims to provide important pedagogical and scholarly implications for researchers in Applied Linguistics in general and ERPP in particular, especially novice scholars and doctoral students that are interested in reflective and reflexive methodologies and orientations.
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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 | 0.159 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.010 | 0.049 |
| Scholarly communication | 0.035 | 0.053 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".