13. Have You, My Little Serpents, a New Skin? Transforming English Studies and the Scholarship of Teaching and Learning
Bibliographic record
Abstract
King and Knight (2010) argue that English Studies' instructors must "articulate and develop their tacit assumptions [about English teaching] and create a discipline-grounded idiom for pedagogical research and reflection" (p. 323). We suggest that the scholarship of teaching and learning (SoTL) invites English educators to reflect more deeply about the assumptions upon which our favoured methodologies are based. At the same time, SoTL's often uncritical reliance on students' umarked voices for many of its insights troubles us. We suggest that while the scholarship of teaching and learning can provide the necessary structure for systematic reflection about English Studies' pedagogies, SoTL would benefit from a more substantial engagement with what English Studies calls theory. In so doing, SoTL can add another critical question to its agenda: "For whom do these practices work?"
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".