Unintentional Bias and Managerial Reporting
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
ABSTRACT We examine the impact of unintentional biases in managerial judgment and in audited accounting information on the reporting of unverifiable private managerial information for stewardship purposes. Such biases are exogenous and irreducible; awareness of their existence does not eliminate bias or lead to heterogeneous beliefs—all agents have common, objective beliefs. We show that any biased managerial judgment in interpreting private information and negatively biased accounting (conservatism) reduce timely reporting of private managerial information by managers. Only positively biased (less conservative) accounting increases such reporting by managers. Contrary to conventional wisdom, negative accounting biases, instead of counteracting the effect of positive managerial bias, act to further reduce reporting by managers and, thus, the supply of timely information to capital markets. Thus, freedom from bias, both in managerial judgment and in accounting, more likely results in managers issuing timely reports.
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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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