Bibliographic record
Abstract
This Article asks the question: what justifies the practice of tort law?It asks the question with a particular focus: which interests should tort protect?This Article argues that tort selects and protects a determinate set of interests, even if we do not take it to be doing so.The second claim advanced in the Article is that tort law is constitutive of political society in the sense that it expresses our sense of ourselves as persons within society, and our sense of what we owe one another.Given that tort law inevitably selects a particular set of interests for protection, and that this selection is politically significant in that it expresses what we take our rights and obligations towards each other to be, this Article argues that the interests tort selects for protection ought to, at least presumptively, reflect the set of interests that are enumerated in constitutions and bills of rights-the interests that best reflect the values that constitute our political morality.Given the fact of reasonable moral disagreement, constitutions and bills of rights offer the best approximation of the values to which †Max Weber Fellow, European University Institute, jean.
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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.006 |
| 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".