From discursive essay to expressive writing: stories of a student and a teacher
Bibliographic record
Abstract
This thesis examines my academic journey through narrative as a student and a teacher in the writing classroom at a major university in Poland and at the Ontario Institute of Studies in Education of the University of Toronto in Canada. I employ an auto-ethnographic approach and arts-based inquiry to discuss and explore my own experiences of learning to write and of learning to teach others to write. I also recount how I rediscovered writing and found my own voice after taking an Expressive Writing course at OISE/UT. I show how personal narrative has taught me to trust my words and reclaim my writing and my teacher-self. My intention for this thesis is to raise awareness that through expressive writing pedagogy and through personal narrative, students develop their voice and confidence.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.026 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".