Performing a Metis Pedagogy in the Rhetoric and Writing Studies Classroom
Bibliographic record
Abstract
Scholars in the field of disability rhetoric (e.g. Dolmage; Price; Vidali) have long called for the denormalization of traditional approaches to the teaching of rhetoric and composition. Such approaches historically characterize rhetoric as disembodied and ask students to compose straight, linear, alphabetic texts which privilege meaning-making through written discourse and remain inaccessible to diverse users and audiences. As a response, this article recounts how I applied the concept of metis—double, divergent, crooked—as a theoretical framework for a special topics course "Disability, Rhetoric, and the Body," and as an alternative pedagogical approach to the teaching of rhetoric and composition. More specifically, this article explores the connection between my own metis-work as a teacher-scholar and my students' performance of metis through multimodal composing and analysis. As a result, the rhetoric and composition classroom becomes a non-normative space where difference is not only valued, but celebrated.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".