Disciples and practitioners of knowledge: a duoethnography on the academic-administrator divide in universities
Bibliographic record
Abstract
Abstract We invite readers into our duoethnography to explore the divide between academics and professional administrators in universities. Guided by duoethnography, a qualitative, collaborative, and dialogic methodology, we drew on our differences, media, and literature to explore the academic-administrator divide. Bringing together a queer administrator, queered by a doctoral program, and a fellow administrator who has not pursued doctoral studies, we started our journey with the Netflix series The Chair . While the series was intended to provide a window into fictional academic life, it was our venture into literature that shaped new insights into the academic-administrator divide. By reframing academics as disciples of knowledge and administrators as practitioners of knowledge, we found new ways to bridge the academic-administrator divide. Using this paradigm to explore literature on peer review, collegiality, and the business school academic-practitioner gap offered us new ways to personally engage with the idea of disciples and practitioners. Throughout the paper, we invite you to participate in the duoethnography and consider whether our insights resonate with your personal experiences and perspectives.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.022 | 0.071 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".