The mock conference as a teaching tool: Role‐play and “conplay” in the classroom
Bibliographic record
Abstract
Abstract In our ostensibly secular age, discussing the real‐world contexts and impacts of religious traditions in the classroom can be difficult. Religious traditions may appear at different times to different students as too irrelevant, too personal, or too inflammatory to allow them to engage openly with the materials, the issues, and each other. In this “Design & Analysis” article Aaron Ricker describes an attempt to address this awkward pedagogical situation with an experiment in role‐play enacted on the model of a mock conference. This description is followed by four short responses by authors who have experimented with this form of pedagogy themselves. In “Conplay,” students dramatize the wildly varying and often conflicting approaches to biblical tradition they have been reading about and discussing in class. They bring the believers, doubters, artists, and critics they have been studying into the room, to interact face‐to‐face with each other and the class. In Ricker's experience, this playful and collaborative event involves just the right amount of risk to allow high levels of engagement and retention, and it allows a wide range of voices to be heard in an immediate and very human register. Ricker finds Conplay to be very effective, and well worth any perceived risks when it comes to inviting students to take the reins.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".