Writing more, better, together: how writing retreats support graduate students through their journey
Bibliographic record
Abstract
The context of higher education in Canada suffers from alarming rates of dropout and prolongation of study programmes. The lack of academic writing ability and feeling of isolation are among aggravating factors impeding on the success of graduate students. Writing retreats are identified as a potential solution to support and improve academic writing output. This article presents an innovative concept designed by a non-profit organisation, Thèsez-vous, specializing in creating physical and human environments to facilitate academic writing. Over the past four years, the organisation implemented and formalized a writing retreat model for graduate students from various fields of study and universities across the Quebec province in Canada. A description of the writing retreats structure and functioning is presented, as well as an analysis of established objectives: 1) progress academic writing based on realistic individual goals; 2) identify optimal writing conditions; and 3) reduce isolation. Based on conclusive findings, the implemented model produces positive results in developing academic writing abilities through a community of practice forming during writing retreats and interacting afterwards. This expanding network of graduate students represents a new generation of researchers, sharing similar challenges with academic writing and collaborating in interdisciplinary settings to progress scientific efforts at large .
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".