Bibliographic record
Abstract
Abstract Many “accounting” scandals have cast some doubts about the truthfulness of the financial statements. After reviewing broadly some theoretical aspects of the question, noticeably the transfer of wealth potentially resulting from an incorrect assessment of the market value of the firm, we look at the principal currents of research in this domain. Firstly we consider researches on Earnings management interested mainly by the level of actual accruals to be compared with a level of “normal” accruals obtained from a predictive model. Another approach consists in estimating thresholds in the distribution of revenues below which the managers will not want to go back. We also look at income smoothing, identified by a variation in the profit inferior to the variation of the sales, or, more simply, to predict a trend expected by the market and see if the profit figure will fall within this limit. Big bath accounting is simply the cleaning of the balance sheet after a change of CEO, for instance. Finally, we look at other approaches to accounts manipulation like those called window dressing or creative accounting , nearer from the professional way of thinking.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".