Bibliographic record
Abstract
Abstract This book continues a decades-long exploration of the theory of private law. Two previous books, The Idea of Private Law and Corrective Justice had presented, respectively, the theory of corrective justice and the analysis of a wide range of specific issues in private law. The present book starts with corrective justice as the structure of the private law relationship and gradually moves outward to situate private law within the wider world of law, dealing with the state’s role in forwarding distributive justice, the horizontal application of constitutional rights to private law, and the rule of law. The book draws on Kant’s legal philosophy to exhibit law, both private and public, as the necessary medium for the reciprocal freedom of all. Central to this enterprise is what Kant called ‘public right’, with its system of public institutions. Throughout these books, four ideas about private law have consistently been in play: (1) fair and coherent reasons for liability are correlative in structure; (2) rights and their correlative obligations provide the content for this structure; (3) the activity of theorizing about private law involves not the construction of a utopia but the understanding of an ongoing normative practice; and (4) a theory of private law is concerned not with producing a determinate code of law, but with explicating the conceptual structure and normative presuppositions of the phenomenon of liability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".