Bibliographic record
Abstract
Over the years I have thought long and hard about ways to structure courses that increase student learning in general and an appreciation for the academic study of religion in particular. My food and religion course best reflects that thinking, and the positive impact that course has had on students encourages me to present it to a wider audience. This chapter presents a Canadian university course that serves as a thematicintroduction to ‘world religions’ while at the same time deconstructing some established notions of religion. The focus is on pedagogy. The ‘diet’ I am prescribing is intended for teachers and their students. The analogy is both fitting and cautionary. The vast majority of food diets fail, except by making some of their creators wealthy. Long-term, sustainable health improvement comes about when a person recognizes that change is required, and when the recommended changes are gradual, multi-faceted and consistent with a person’s physical and emotional makeup. A key point I make below about course construction and delivery is that students are more apt to reconsider their understanding of religion if those changes help them make better sense of the world. The pedagogical challenge is to help students recognize the complexity of what is typically understood as religion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".