Engaging Students as Thinkers and Writers in Every Discipline 11th Annual Dalhousie Conference on University Teaching and Learning
Bibliographic record
Abstract
I’d like to start by thanking the conference organizers for asking me to speak here today. As I will argue in what follows, every invitation to write is – or can be – an invitation to think, to reflect, and to learn. The elements of any writing task – the topic, the audience, the occasion, the purpose – interact to construct a unique intellectual and social opportunity. As I was writing this paper, with this moment and this audience in mind, I thought in particular ways about my 30 years in the field of Writing Studies and about what we know about writing and why that knowledge matters. In the past three decades, there have been dramatic developments in the study and teaching of writing. Theorists, researchers, and teachers have created a complex and detailed account of writing by drawing on a rich variety of sources, including the classical rhetorical traditions of Greece and Rome, contemporary studies of cognition, the sociology of knowledge, research into academic and workplace writing, new literacy theories, the digital revolution, and the current cross-disciplinary fascination with discourse. The result is a body of knowledge about writing that has profound practical and pedagogical implications for teaching, thinking, and learning across the curriculum.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 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".