Writing Retreats Responding to the Needs of Doctoral Candidates Through Engagement with Academic Writing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it