Political and Naturalistic Conceptions of Human Rights: A False Polemic?
Bibliographic record
Abstract
What are human rights? According to one longstanding account, the Naturalistic Conception of human rights, human rights are those that we have simply in virtue of being human. In recent years, however, a new and purportedly alternative conception of human rights has become increasingly popular. This is the so-called Political Conception of human rights, the proponents of which include John Rawls, Charles Beitz, and Joseph Raz. In this paper we argue for three claims. First, we demonstrate that Naturalistic Conceptions of human rights can accommodate two of the most salient concerns that proponents of the Political Conception have raised about them. Second, we argue that the theoretical distance between Naturalistic and Political Conceptions is not as great as it has been made out to be. Finally, we argue that a Political Conception of human rights, on its own, lacks the resources necessary to determine the substantive content of human rights. If we are right, not only should the Naturalistic Conception not be rejected, the Political Conception is in fact incomplete without the theoretical resources that a Naturalistic Conception characteristically provides. These three claims, in tandem, provide a fresh and largely conciliatory perspective on the ongoing debate between proponents of Political and Naturalistic Conceptions of human rights.
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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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.081 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".