Bibliographic record
Abstract
Abstract In Western culture, animal ethics has traditionally emphasized acts of deliberate cruelty and, in the twentieth century, institutionalized harms to animals through activities such as meat production and biomedical research. However, with a large human population and technologies that developed mostly during the last century, a new set of harms—unintended and often acting indirectly—now injure and kill vast numbers of animals. Unintended harms arise from human artifacts such as cars, windows and communication towers. Indirect harms occur from disturbances to the balances and processes of nature, for example through pollution, introduction of alien species and climate change. These harms will undoubtedly increase unless they become a focus of attention and mitigation. A new animal ethic is needed to incorporate these harms into ethical thought. It will need to address such issues as responsibility for unintended versus intended harms, and for collective versus individual actions, and it will greatly narrow the gap between animal ethics and environmental ethics.
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 | 0.033 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.064 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 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".