Writing Retreats Responding to the Needs of Doctoral Candidates Through Engagement with Academic Writing
Bibliographic record
Abstract
During dissertation writing, PhD candidates face challenges engaging with academic writing, among other things, which leads to their participation in writing retreats with their peers. Developing a better understanding of PhD candidates’ needs to optimize engagement with writing is important for improving the overall doctoral experience and reduce attrition. We then conducted a qualitative longitudinal experimental study with PhD candidates from Canadian universities: 15 respondents who participated in a writing retreat and 15 respondents who never participated in such event. Based on our findings, this article presents a complementary perspective to the theoretical model of engagement with writing by Murray (2015). Thereon, we expand on the intersectionality of components (cognitive, physical, social) to illustrate the influence of structured writing activities. These intersections highlight the benefits of writing retreats to answer the needs of PhD candidates to engage with writing: planning dedicated writing periods, implementing effective work methods in environments enabling concentration, and engaging with collective writing activities. By way of supplementing the most recent literature on the subject, we suggest that the participation in structured writing retreats serves as a pedagogical benchmark for graduate programs to offer students comparable conditions in support of their writing requirements to enhance academic success.
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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 | 0.025 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".