Teaching with Complicating Views: Beyond the Survey, Behind the Pro and Con
Bibliographic record
Abstract
Abstract In this article I propose a method of selecting and assigning readings in the religious studies or theology classroom, such that these readings complicate one another, rather than standing in opposition or as simple alternatives. Such a strategy emulates key pedagogical insights of twelfth‐century sentence collection, an activity at the very heart of the earliest universities inEurope. It also draws support from the theories of intellectual development advanced byWilliamG.Perry, Jr. and theWomen's Ways ofKnowingCollaborative. Both precedents suggest a principle of “complicating views” that can be flexibly employed in a variety of ways and diverse pedagogical contexts, as illustrated by examples from several classes. Such strategies aim to avoid reinforcing intellectual patterns of dualism or undifferentiated relativism; instead, they attempt to promote students' ability to integrate discordant voices and to appreciate diverse points of view, while also staking their own claims relative to them.
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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 | 0.034 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".