Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We incorporate normative motivations into the unilateral precaution model of tort. Individuals have moral concerns about causing harm and would like others to believe that they do. In the absence of legal liability, causing harm suggests low concerns and is therefore damaging to one's social image, which feeds back into incentives to take precautions. These nevertheless remain suboptimal when informal motivations are not strong enough for injurers to willingly compensate victims ex post. By contrast, perfectly enforced legal liability crowds out informal motivations completely (e.g., tortfeasors suffer no disesteem) but precautions are then efficient. Under imperfect enforcement, informal motivations and legal sanctions complement one another. With strict liability, individuals held liable suffer disesteem, there is some motivational crowding-out but no net crowding-out with respect to overall incentives. Under the negligence rule, there is motivational crowding-in when image concerns induce bunching on the legal due care standard.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it