Assessing Rawls' Difference Principle as Practical Guidance for our Duties to Animals
Bibliographic record
Abstract
It is a commonly held view that Contractarian ethics cannot produce a substantial moral system that includes animals. However, since Mark Rowlands introduced his interpretation of Rawlsian Contractarianism we have been provided with, as it were, a ready-made system to which we can now insert animals. However, such a result is far from the final hurdle in providing a substantial account of human duties to animals. The extension of justice to animals may well be possible, but it produces a great many intricate problems as to how the dynamics of human-animal relationships should be borne out. It would seem now that the animal-friendly Contractarian owes an account of how our relationships to animals be governed. In this essay I will argue that the Rawlsian Difference Principle lends itself to just such a task. The Difference Principle’s focus on equal consideration without a need for identical treatment lends itself to producing comprehensive and flexible guidelines for our duties to animals - providing pragmatic answers to how we should engage with them. The conclusion of this paper will not be by way of an entire theory governing human-animal relations (as I’m without time or space to do justice to such a project), but an argument for establishing the viability of the Difference Principle as the guiding notion behind such a theory.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".