Probing normalized institutional discourses about writing: The case of the doctoral thesis
Bibliographic record
Abstract
Over the past decade, much government and institutional interest internationally has focused on the expansion and improvement of doctoral education, with degree completion rates and times topping government and university agendas. Since degree completion is intimately linked to the thesis, doctoral writing has surfaced as a new problem space for institutional attention and intervention. These interventions, as well as the roles assigned to teachers and researchers of writing, language, and academic development, however, depend largely on how institutions conceive of writing, which in turn is shaped by normalized inherited discourses about writing. Drawing on rhetorical theories of discourse and writing, this article examines institutional discourse for how it conceives of the doctoral thesis, how it regulates the writing of the thesis, how it positions the process and product of thesis writing within the knowledge-making activities of the university, and what implications this discourse has for how institutional interventions in support of doctoral writing are conceptualized. Using the example of discourse about doctoral thesis writing offered by graduate schools at research-intensive universities in Canada, the article works from a systemic perspective that invites all those involved in facilitating research education to examine, reflect on, and contemplate institutional discourses about writing as inherited and normalized patterns of social practice. Finally, the article argues that these practices have significant consequences for doctoral scholars, supervisors, and the ability of institutions to develop new visions for curricular innovation in research education.
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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 | 0.070 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.050 | 0.127 |
| Scholarly communication | 0.033 | 0.022 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.011 | 0.019 |
| 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".