Bibliographic record
Abstract
Let us be clear from the outset: This book is not intended in any way as an apology for carefree meat consumption, nor is it intended as a condemnation of eating animals.For any hardline carnivores or vegans out there seeking to find material that bolsters their claims of superior dietary practices -look elsewhere!If anything, our foremost intention as editors -and that of our contributors -is to reiterate the importance of thinking carefully about what you eat, where it comes from, and how it was produced.What are the impacts of your diet on you, your community/communities, and your planet?This critical thinking about food and diet is a responsibility that we believe we have as editors, who are part of a consumption-driven class of settlers in what is presently known as Canada, all while living in a particularly challenging epoch in terms of global environmental change -and the social, political, and economic changes that come along with it.From this starting point, however, each of the contributors to this volume travels in a different direction.Some of us have arrived at "no-meat" or "low-meat" diets after embarking upon this careful dietary calculus; others of us have been less concerned with the material presence of meat on our plates and more with the qualitative aspects of its production (which, in turn, usually has quantitative implications).In bringing together this variety of perspectives on what constitutes "green meat, " and in considering whether the practice of "eco-carnivorism" is even possible in the first place, we hope to provide fodder (sorry, this is the first of many puns, intended or otherwise, that result from a book themed around food and animal agriculture) for the various debating voices in your head as they reach a synthesis regarding dietary practices that work for you and your attempt to limit your dietary footprint.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.614 | 0.472 |
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".