Are rights really so wrong? A response to Nigel Biggar’s <i>What’s Wrong with Rights</i>
Bibliographic record
Abstract
In my response to Nigel Biggar’s book What’s Wrong with Rights, I argue that an epidemic of rights-fundamentalism does not require the complete rejection of all rights language. Rather, it is possible to use rights language in a way that reconceptualizes and broadens our understanding of duty, and advances our moral discourse and growth in virtue, rather than hindering it. To demonstrate this point, I contrast Biggar’s example of a problematic ruling by the Canadian Supreme Court with a more thoughtful and nuanced approach to rights language demonstrated by a series of cases on free speech in schools issued by the U.S. Supreme Court. I also offer a re-reading of Francisco de Vitoria’s development of rights language to argue that his presentation of rights overcomes many of Biggar’s critiques.
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 | 0.015 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.026 | 0.045 |
| 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".