Biotechnological fixes and the Big Three urgent moral challenges facing the global livestock industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it