You Are What You Eat: Religion, Meat, and the Moral Dilemma
Bibliographic record
Abstract
In the last twenty-five years, the Muslim population of Canada has tripled. The value of the Canadian halal food market is now estimated to exceed one billion dollars. In 2021, eight in ten Indian adults reported that they restrict their meat eating in some way for a cultural reason. The same year, 34% of Americans surveyed—Jewish and non-Jewish—said they bought kosher meat out of a concern for food safety. At first glance, the world’s meat market is far from religious. But upon closer inspection, one can see that the history of meat eating is not only about survival but also about cultural bonds, personal health, and the theological clash between good and evil. The variances in meat consumption between the Islamic, Jewish, and Hindu faiths distinctly display these themes. These behaviors surrounding meat eating demonstrate, on a small scale, what it is to be a human being in a society, and how our everyday actions indicate our deeper desire to do “right.” Panel info: High Stakes: Faith, Control and Consumption Moderator: Professor Natan Meir
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".