A forensic accounting em Portugal : evidências empíricas
Bibliographic record
Abstract
Fraud is a fact of corporate life, including occupational fraud.Institutions and society tend to assign responsibility for its detection and prevention to the auditors, even when they are not able to perform these functions.This audit expectation gap requires rethinking the audit, external and internal.In order to overcome many of the difficulties resulting from this gap we analyzed the Forensic Accounting in its state of the art theory and professional practice.Recognized the greater importance of Forensic Accounting in detecting and preventing fraud is urgent to know its existence in Portugal.Noting that the legislation, educational practices and institutions in Portugal do not recognize the Forensic Accounting, we have tried to know if it has, in practice, some reality in Portugal.For this purpose, we have studied the skills, knowledge and actions of Internal Auditors, Bankruptcy Trustees and the Criminal Police in Portugal, using a survey for the first two groups, and a structured interview for the rest.A quantitative analysis of responses allows us to conclude, in a detailed and specific approach, to each group, if in Portugal is carried out work that can be seen in the context of Forensic Accounting.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".