Dominion, Stewardship and Reconciliation in the Accounts of Ordinary People Eating Animals
Bibliographic record
Abstract
Despite the growing popularity of vegetarian foods and diets, the vast majority of people in North America and other parts of the affluent world still eat meat. This article explores what ordinary people think about eating animals and how they navigate the ethical questions inherent in that praxis. Drawing from interviews with 24 people living in Ottawa, Canada, the study shows how the concepts of dominion, stewardship and reconciliation manifest in the everyday lives of ordinary people as models for human relations with nonhuman others and the environment. These ideas resonate in the lives of ordinary people, both religious and nonreligious, and entwine as people try to make sense of how to live with the fact that their everyday food consumption causes suffering and harm. This study shows that in the context of everyday life, dominion, stewardship and reconciliation are not alternative views, but connected to each other, and serve different purposes. The study highlights a need for analyses that constitute practical ways to renew the broken relationships within creation and which incorporate nonreligious people into the scope of analyses that focus on the relationships between humans and nonhuman creation.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.076 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".