Dissertation Pedagogy in Theory and Practice: Extending Our Roundtable
Bibliographic record
Abstract
In this paper, we extend our roundtable session from the 2019 Canadian Writing Centre Association Conference in Vancouver, which ignited dialogue about how writing centre practitioners and educational developers can help faculty review and strengthen their approaches to providing feedback on graduate student theses and dissertations. We discuss how we designed and delivered an instructional development workshop for faculty at our university to strengthen their approaches to supporting graduate student thesis and dissertation writers. In doing so, we aim to foster further dialogue about how writing centre professionals and educational developers can partner with faculty to enhance and develop their approaches to providing feedback on large-scale writing projects.
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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 | 0.182 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.014 |
| Scholarly communication | 0.028 | 0.028 |
| Open science | 0.009 | 0.040 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.041 | 0.009 |
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".