Speaking Against Inequity in the Writing Classroom: Challenging the Performance Paradigm for Undergraduate Oral Presentations
Bibliographic record
Abstract
Many existing scholarly speaking pedagogies continue to think of oral presentations as performances for an audience rather than dialogic exchanges of research. Such approaches, prominent in Canadian universities, can exacerbate classroom inequities by valuing certain ways of speaking and, by extension, certain speakers: speaking pedagogies, for example, that instruct students to speak “clearly,” dress “professionally,” or even to appear “confident,” can encode prejudices that privilege some voices and bodies over others, perpetuating discrimination based on gender, race, sexuality, language, and culture. This article argues that an equitable scholarly speaking pedagogy will teach students to instead think of oral presentations as opportunities for collaborative knowledge-making. We offer strategies to help students think of scholarly speaking as an integral part of the research process, rather than a stand-alone performance. Drawing on genre-theoretical approaches to academic writing, we argue that this shift can be achieved by using “precedents”—examples of scholarly speaking—to familiarize students with academic oral discourse’s genre conventions, helping students to recognize scholarly speaking as a situated and dialogic research genre. By shifting the goal of academic oral presentations towards cooperative knowledge-making, these strategies at once challenge student prejudices about who can be a “good” speaker and remind students of their responsibilities as audience members, resulting in a more equitable and inclusive classroom environment. To provide a pragmatic example of our approach, we outline the “Classroom Conference” assignment that we developed and evaluated, which uses precedents to prepare students for the oral presentations that are integrated as a step in completing their larger research projects. Analyzing survey data from students who completed this assignment, we recognize its success but also propose strategies for overcoming persisting challenges in getting students to shift their thinking toward an equitable model of scholarly speaking.
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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 | 0.041 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.023 |
| Scholarly communication | 0.032 | 0.013 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".