Moving In-Class Debates Online: Deliberating Contentious Issues in an Asynchronous Classroom
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ensuring that students in asynchronous classrooms are afforded similar opportunities to develop business-relevant skills and knowledge has become an increasingly important area as many universities have expanded their online course offerings. In this article, I document a format translation of an in-class debate into a highly flexible and generalizable exercise for an asynchronous classroom. Drawing on previous work on in-class debates and rhetorical strategies, I adapt the in-class group debate format, to an asynchronous undergraduate Human Resource Management course, to foster active learning among students while tackling contentious subjects within the field. Moreover, this versatile exercise can be applied effectively in a wide variety of management courses. I begin by outlining the benefits of debates in a business education context, describe the learning objectives of the exercise, offer numerous sample debate questions, and conclude by providing all relevant teaching notes and instructions.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it