Biotechnological fixes and the Big Three urgent moral challenges facing the global livestock industry
Bibliographic record
Abstract
The current global food system, and in particular the livestock industry, has been effective at providing low-cost calories to large segments of the population, but it also causes significant harms and poses serious risks. In particular, the global food system currently likely causes billions of animals to suffer every year, significantly contributes to climate change, and threatens public health via the possibility of zoonotic disease. There are many other problems that have been identified with the livestock industry, but these three threats, which I refer to as the Big Three, are among the most urgent moral issues in the world. Significant progress could be made to address all three of these risks if the global population moved to a primarily plant-based diet. However, there are reasons to believe this possibility is unrealistic given current consumer preferences and political realities. As an alternative, one could ask whether an approach relying entirely on novel biotechnology could be used to address the urgent moral challenges of the global livestock industry without substantially changing the consumer experience or facing political backlash. In this paper I consider what such a scenario would look like, and argue that failing to address any one of these three major issues would be a serious moral failing. Though many other suggestions have been made looking at how biotechnology might address individual issues, this paper suggests that in order to avoid the need for difficult behavioral and political changes, biotechnological solutions would ultimately need to be developed that address welfare, environmental, and public health concerns.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".