Bibliographic record
Abstract
Abstract Prior research indicates that individuals acting as jurors experience outcome effects in audit negligence litigation. That is, jurors evaluate auditors more harshly in light of negative outcomes, even when audit quality is constant. I posit that outcome effects in this setting are caused by jurors using their negative affect (i.e., feelings) resulting from learning about negative audit outcomes as information relevant to auditor blameworthiness. I tested this hypothesis in an experiment in which I manipulated audit quality, outcome information, and provision of an attribution instruction. The attribution instruction was designed to discredit negative affect as a cue to auditor blameworthiness. Consistent with expectations, attribution participants' evaluations of auditors exhibited less reliance on outcome information and more reliance on audit quality information than did evaluations made by control participants. In fact, outcome effects were eliminated for attribution participants. Courts may be able to improve the quality of jurors' decisions in such cases by employing an attribution instruction.
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How this classification was reachedexpand
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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".