The Oneiro- and the Hagio-: Teaching about Dreams from the Standpoint of Comparative Hagiology
Bibliographic record
Abstract
This paper explores the potentially fruitful interplay between a set of practical and theoretical approaches developed to teach post-secondary students about accounts of dreams and of exemplars, in cases where these phenomena have been deemed significant by specific religious discourse communities. Incorporating insights from his participation in the Comparative Hagiology group, the author suggests—in particular—that the expanded perspective on hagiography proposed by Rondolino, Hollander, and others can serve as a fruitful vantage from which to survey both of these phenomena in the classroom, revealing some intriguing correspondences between them. The author concludes by proposing some ways that the comparative hagiological classroom could be a particularly productive learning environment, and one that directly addresses some of the challenges of contemporary post-secondary education (from both the instructors’ and students’ perspectives).
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".