Collaborative Dyadic Autoethnography: A Meta-Reflection on Teaching Epistemology to Graduate Students
Bibliographic record
Abstract
The teaching of epistemology to graduate students can be a daunting task because of its complex and evolutive nature. Yet, the development of an epistemological posture by graduate students is associated with higher rates of academic success. In this article, we share a reflection on our perspectives and experiences with teaching epistemology in two study programs at the Faculty of Education of a Canadian university. This object of inquiry is approached through a collaborative autoethnographic methodology involving us (the authors) in dyadic interviews. The authentic exchanges between Mariette (full professor teaching epistemology to doctoral students) and Catherine (former doctoral student teaching epistemology to master’s students) reveal similarities in pedagogical practices, but differing goals in knowledge transmission. Based on the transformative learning cycle, insights discovered in our capacity of teachers and researchers are discussed to illustrate individual and shared transformations. These insights forge links between the transformative autoethnography model and the teaching of epistemology. They also highlight the importance of context when teaching this subject and awareness of its personal and professional meanings. In particular, we remain sensitive to issues of justice and equity, considering that we are involved in the development of emerging scholars in a francophone minority context. In addition to contributing to the scientific literature related to teaching epistemology in higher education, we highlight the benefits of a collaborative autoethnography to study the object of inquiry and foster a dialog within the academic community to develop pedagogical practices.
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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 | 0.053 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".