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
Studies have documented racial and gender-based disparities in civil jury awards. Legal scholars have raised concerns that biases might be especially prevalent in awarding pain and suffering damages, which are particularly open-ended and difficult to estimate. We contribute to this body of literature by providing experimental evidence of a causal relationship between the perceived race and gender of victims, the perception of their pain and suffering, and the damages awarded to them. We focus on two types of injuries: head and knee injuries, on the intersection of gender and race and on related evaluations of victims’ behavior. We find that people perceive the pain and suffering of White victims to be greater than that of Black victims afflicted by the same head injury. The most alarming finding of our experiment is that Black male victims receive significantly lower amounts of damages for pain and suffering associated with both head and knee injuries compared to all other victims. By contrast, Black female victims are not penalized compared to White women and men, and receive significantly higher amounts of damages for their pain and suffering associated with both head and knee injuries compared to Black men.
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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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