Bibliographic record
Abstract
This paper proposes a new full-scale simulation approach for correctly measuring the detection ability of the modified Jones model (MJM). For ideal evaluation accuracy, we define perfect prior information on whether a firm under investigation manipulates its earnings with the allowance for doubtful accounts by differentiating firms that engage in no earnings manipulation from those that do. The MJM shows a detection rate of only 49.1%, which is lower than 50% for a fair coin toss, and thus it is not reliable. This low detection rate is due to the oversensitivity of the MJM, which causes it to misinterpret long-term increases in sales revenue from normal business activity as earnings management. Therefore, the MJM has a tendency to incorrectly detect non-discretionary accruals (NDACs) as discretionary accruals (DACs) and thus misjudge a clean firm as a dirty one, because it shows a low (moderately high) detection rate for clean (dirty) firms.
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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 | 0.003 | 0.001 |
| 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 | 0.001 | 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 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".