Bibliographic record
Abstract
Although online discourses about dissertation writing (i.e., you should be writing memes) offer students levity, they function in stark contrast to how dissertation writing is treated in real life. Canadian education scholars with PhDs have examined the student-supervisor relationship (McAlpine & Weis, 2000), collaborative writing spaces (Eaton & Dombroski, 2022; Ens et al., 2011), and the overall difficulties of the dissertation process (Bayley et al., 2012; Walter & Stouk, 2020), but we have yet to locate literature on the perspectives of Canadian education PhD students who have generated online communities of practice to engage in their dissertation writing. To obtain better understanding of our personal relationships to writing and virtual communities of practice, we established an online writing group during the summer of 2023 where we wrote our respective candidacy proposal and dissertation chapters while also reflecting on and responding to prompts about the process of writing. This reflection on our writing practice concludes that if PhD students feel un(der)supported by institutional writing communities, or if said communities are not available, constructing their own community will be beneficial to their writing goals
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.
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 | 0.007 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 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".