Practical considerations for establishing writing groups in interdisciplinary programs
Bibliographic record
Abstract
Abstract Academic writing capabilities are a cornerstone of success in doctoral programs, yet prove to be a point of anxiety and apprehension for many students. Providing support for academic writing within interdisciplinary programs poses special considerations, as students in these programs are called upon to transcend single disciplinary perspectives to address a central area of research, and to integrate multiple different disciplinary perspectives that may be conflicting or overlapping. When treated as a social practice, writing can serve as a common interest that draws doctoral students to convene and develop in their learning. This article describes the development of a student writing group in an interdisciplinary doctoral program, considering how the characteristics and activities of the group create an environment that enables and encourages enhanced interdisciplinary learning. The article argues that, when delivered successfully, student writing groups have the potential to strengthen student writing skills and outputs, as well as deepen interdisciplinary learning. Drawing from Lattuca’s four aspects of interdisciplinary learning (relational, mediated, transformative and situated), the article illustrates ways that the writing group helped to promote each aspect of learning and benefit the overall student experience in the program. Reflecting on these experiences, the authors propose six practical considerations for establishing writing groups in interdisciplinary programs: vision and purpose; dedicated time and space; institutional support; readings or educational material; socialization opportunities; and shared responsibility. Administrators, students, faculty members and support staff involved in the delivery of interdisciplinary doctoral programs are called upon to consider the introduction and/or strengthening of writing groups for the purpose of enhanced interdisciplinary learning.
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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 | 0.155 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.029 | 0.012 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.009 | 0.025 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".