Four types of activities that affect animals: implications for animal welfare science and animal ethics philosophy
Bibliographic record
Abstract
Abstract People affect animals through four broad types of activity: (1) people keep companion, farm, laboratory and captive wild animals, often while using them for some purpose; (2) people cause deliberate harm to animals through activities such as slaughter, pest control, hunting, and toxicology testing; (3) people cause direct but unintended harm to animals through crop production, transportation, night-time lighting, and many other human activities; and (4) people harm animals indirectly by disturbing ecological systems and the processes of nature, for example by destroying habitat, introducing foreign species, and causing pollution and climate change. Each type of activity affects vast numbers of animals and raises different scientific and ethical challenges. In Type 1 activities (keeping animals), the challenge is to improve care, sometimes by finding options that benefit both people and animals. In Type 2 activities (deliberate harm), the challenge is to avoid compounding intentional harms with additional, unintended harms, such as animal suffering. For Type 3 and 4 activities, the challenges are to understand the unintended and indirect harms that people cause, to motivate people to recognise and avoid such harms, and to find less harmful ways of achieving human goals. With Type 4 activities, this may involve recognising commonalities between animal welfare, conservation and human well-being. Animal welfare science and animal ethics philosophy have traditionally focused on Type 1 and 2 activities. These fields need to include Type 3 and 4 activities, especially as they increase with human population growth.
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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 | 0.035 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.107 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".