Bibliographic record
Abstract
Dignity often serves as the cornerstone for a justification of rights. However, it has been criticised for its exclusion of nonhuman animals and many human individuals: dignity is traditionally grounded in a capacity that some but not all humans and animals possess, e.g. rationality. To successfully overcome this problem of exclusion, this article argues that we should adopt an account of sentient dignity, i.e. an account of dignity based on sentience alone. The article thus makes three contributions. First, it demonstrates that the basis of dignity has yet to receive a plausible justification. To illustrate this, it outlines the problem of exclusion, and it exposes three problems with a prominent solution offered by Pablo Gilabert. According to Gilabert's view, dignity should be based on several valuable capacities including rationality, sentience and cooperation, among others. However, basing dignity on several capacities (i) risks over-inflating the scope of dignity, (ii) struggles to account for internal complexity, and (iii) produces problematic moral distinctions. Second, the article argues that sentient dignity overcomes these three problems whilst being plausibly inclusive. Finally, it contends that an account of sentient dignity vindicates the non-redundancy of dignity, renders sociopolitical discourse philosophically coherent, and harnesses dignity's potential strategic value.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.052 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| 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".