Auditors' Strategies to Protect Their Litigation Reputation: A Research Note
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
Litigation may be harmful in terms of direct costs such as damages and defense costs, as well as indirect costs such as harming the auditor's general reputation and name. When a case is initiated, the auditor may choose to settle out of court or fight. Often settling is less costly in the short run, but may be costlier in the long run as the auditor develops a reputation for not fighting, thus, inducing greater future litigation. This study investigates whether reputational concerns for future litigation motivate auditors to strategically take costly actions to fight rather than settle. I use an experiment involving 48 partners to examine auditors' actions in a situation where auditors report their litigation outcomes to future litigants, allowing them to develop reputations, and a situation where auditors do not report their litigation outcomes. As predicted, I find that auditors take costly actions to protect their litigation reputation. Auditors are more likely to predict that their side will win and have more difficulty settling, even if settling is less costly, when reputation can be protected as compared to when it cannot. In contrast, inexperienced auditors are not influenced by reputation concerns. Thus, litigation reputation concerns that influence auditors' decisions and actions appear to develop with an auditor's experience and tenure in the audit profession.
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 | 0.019 | 0.203 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.000 |
| 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 it