Chapter 6 - The Value of Values: The Importance of Autonomy in Contractarian Reasoning
Bibliographic record
Abstract
Jan Narveson has long advocated contractarianism as the only viable theory of justification in moral and political contexts. In this he is correct. Narveson further thinks that libertarianism is the substantive moral view entailed by contractarianism, given a set of assumptions about human nature and the circumstances of human interaction. In this he is mistaken, or so I suggest. The reason Narveson is led to the erroneous conclusion that libertarianism follows from contractarianism is his employment of an inadequate conception of ‘value’ at the heart of contractarianism justifications of normative constraints. Contractarians justify a set of moral rules on the basis of their service to the values of individuals, but the resulting theory depends for its normativity on the strength of the theory of values upon which it is built. I argue that if values are to play the role assigned to them in contractarian accounts then they cannot include all desires or ends an agent might have, but instead they must be restricted to autonomous desires. I then (begin to) show that one must derive very different substantive conclusions from the contractarian methodology, using a few of Narveson’s own discussions of substantive issues such as drug legislation, education and information policy.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".