Critically reflecting on and through creative practice in doctoral education: a collaborative autoethnography of journey mapping
Bibliographic record
Abstract
Purpose This paper aims to present journey mapping as a creative practice that can be used to “do doctoral education differently”, specifically, in a way that supports the wellbeing of doctoral students and centres students often excluded in post-secondary planning and program development. It understands journey mapping through the lens of feminist care ethics, critical and decolonizing disability studies, theories of Indigenous relationality, systems thinking, and action-oriented approaches. Design/methodology/approach Using collaborative autoethnography, it critically analyses authors’ experiences of a journey mapping process initiated by students in a new interdisciplinary doctoral program in Ontario, Canada. For this study, the authors invited all students currently enrolled in the program and the Program Director to share their reflections on their experiences with journey mapping as a creative practice. They then conducted collaborative data analysis, working together to identify common themes, experiences and tensions which arose throughout the journey mapping process. Findings The study analysis positions journey mapping as a creative practice of collective memory, which can facilitate connection, healing and change. It suggests that this practice can be used to resist problematic ideals of individualism, and competition within academia, by offering a process through which graduate students can build community, advocate for programmatic changes, and move towards individual and collective wellbeing. Originality/value Drawing on the lived experiences in an interdisciplinary doctoral program, this paper brings together work that explores student experience and creative practice in graduate education with the practice of journey mapping, to highlight the possibilities and tensions of using this approach. In the changing landscape of doctoral education, practices that centre students’ voices and support student wellbeing must be developed, and the resources needed to support such practices better understood.
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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 | 0.031 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".