A Trioethnographic Exploration of Co-Authoring in Higher Education: Perspectives of a PhD Student, a Supervisor, and an Editor
Bibliographic record
Abstract
As co-authoring becomes more prevalent internationally and across disciplines, there is a pressing need to examine the experiences of co-authors to understand how differing author identities and hierarchies may affect each author’s experience. This trioethnographic study, in which three researchers explored their experience of co-authoring through dialogic inquiry, offers a critical, reflective analysis of three publication stakeholders: a doctoral student, a doctoral supervisor, and a journal editor. Key findings emerge under two themes: ‘Working together benefits us all’ and ‘Working together isn’t always easy.’ While co-authoring fosters synergy, motivation, collegiality, and professional growth, it also surfaces challenges related to working styles, power dynamics, and cultural differences. These findings urge the authors to question existing beliefs about power imbalances within co-authoring relationships and challenge the expectations placed on PhD students and supervisors, with the aim of achieving more inclusive and sustainable publishing practices. Methodologically, this study promotes trioethnography as a legitimate approach to investigate relationships across cultural and geographical borders.
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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 | 0.035 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.031 | 0.025 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.010 |
| 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".