Bibliographic record
Abstract
What imaginings of the doctoral writer circulate in the talk of doctoral researchers and their supervisors? How do institutional policies and the conventions of particular disciplines shape the ways in which doctoral writing is imagined? Why, and in what ways, has doctoral writing been re-imagined in the twenty-first century? What future imaginings of doctoral writing may be hovering on the horizon? This edited collection has gathered a diverse group of authors—from Aotearoa New Zealand, Australia, Bangladesh, Japan, South Africa, the UK, Denmark, Canada, and the US—to consider these challenging questions during a time in which doctoral education is undergoing enormous transformation. Together, the contributors to this collection explore how the practice of doctoral writing is entangled with broader concerns within doctoral education, including attrition, timeliness, the quality of supervision, the transferability of knowledge and skills to industry settings, research impact, research integrity, and the decolonization of the doctorate
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 | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.049 |
| Scholarly communication | 0.027 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".