Bibliographic record
Abstract
Contrary to critics and advocates of contractarianism alike, I argue that mutual advantage contractarianism entails rights and protections for animals. In section one I outline the criteria that must be met in order for an individual to qualify for moral rights on the contractarian view. I then introduce an alternative form of ‘rights,’ which I call ‘protectorate status,’ from which an individual can receive protections indirectly. In section two I suggest guidelines for assigning animal rights based on two ways of categorizing animals. On the basis of the categorization according to benefit derived, I argue that animals used for companionship, security, hunting assistance, transportation, entertainment, medical service, nourishment, or clothing will tend to qualify for basic rights against starvation, predation, and disease. On the basis of the categorization according to species, I argue that, on top of the basic rights above, dogs tend to qualify for rights against abuse, and against frivolous medical experimentation, as well as further negotiated rights. Cows have the basic rights against starvation, predation, and disease, but squirrels and bears have no rights. In section three I argue that some animals qualify for protectorate status, which would establish various protections for different animals, but would also generally prohibit cruelty towards animals.
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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.058 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".