The Arguments in <i>Justice in Transactions</i>: A Reply to Commentators
Bibliographic record
Abstract
Abstract This reply addresses some of the basic questions and criticisms raised by commentators in their interesting pieces on Justice in Transactions. My aim in that book is to work out a public basis of justification for the common law of contract. Given the limits of space, the discussion here is unavoidably selective and incomplete. Within these parameters, however, the article presents, and hopefully clarifies, some of the book’s main arguments that are relevant to the comments, using the footnotes for more detailed responses to the particular points made. These points encompass both methodological and substantive issues. The former center around the nature of public justification and whether the proposed theory of contract law meets its requirements. The substantive issues addressed include the role of promises in contract law, the compatibility between contractual fairness and contractual freedom, and the relation between contract and distributive justice.
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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 | 0.055 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.037 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.063 | 0.078 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".