Other‐Consciousness and the Use of Animals as Illustrated in Medical Experiments
Bibliographic record
Abstract
abstract Ethicists such as Peter Singer argue that consciousness and self‐consciousness are the principal considerations in discussing the use of animals by humans, such as in medical experiments. This paper raises an additional consideration to factor into this ethical discussion. Ethics deal with the intentional impact of subjects on each other. This assumes a meta‐representational ability of subjects to represent states of mind of others, which may be termed other‐consciousness. The moral weight of other‐consciousness is manifest in the notion of responsibility, where humans lacking in other‐consciousness (such as individuals with autism) may not be held responsible for their harmful actions towards others. As responsibility implies not only duties but also rights and more generally high moral status, it follows that other‐consciousness grants high moral status, other things being equal — recognizing that other factors grant moral status too. Other‐consciousness also increases the capacity for suffering, both due to increased freedom (and consequently increased possibility of restriction of freedom) and to increased empathy (with suffering of others). Hence, the more an animal is other‐conscious, the more it deserves high moral status and the more it can suffer, other things being equal, and consequently, the less it should be used for human purposes. Further study is required to elucidate to what extent animals used by humans, such as in medical experiments, particularly primates and other highly evolved mammals, are other‐conscious.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".