The Morphology of the SoTL Article: New Possibilities for the Stories that SoTL Scholars Tell About Teaching and Learning
Bibliographic record
Abstract
The folklorist Vladímir Propp identified a curious phenomenon in his study of 100 Russian fairy tales: despite their tremendous surface variety, they followed a single narrative structure or morphology. This article argues that the same phenomenon applies to SoTL articles: despite the tremendous variety of content and methods that SoTL articles evince, they have come to tell the same kind of story. They tell, over and over, a story of redemption. I identify two problems with the story of redemption, the first having to do with ethos (the character that an author projects to their readers), and the second having to do with plausibility. I propose an array of narrative possibilities to enable SoTL authors to tell other kinds of stories — possibilities based on problematizing rather than easily solving. I argue that these possibilities better realize how some of the foundational thinkers in SoTL wanted the field to evolve. While benefiting all SoTL practitioners, such an expansion of narrative possibilities will make the field a more welcoming place to humanities scholars in particular, many of whom share a skepticism about the possibility of linear progress and perpetual self-improvement.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".