Bibliographic record
Abstract
Financial statements are not as important to investors as they once were, as technology has changed the way companies create value today. While these changes pose serious threats to the economic viability of auditing, they also create new opportunities for auditors to pursue. Both the American Institute of Certified of Public Accountants and the Canadian Institute of Chartered Accountants (CICA) Task Force on Assurance Services have identified continuous auditing as a service that should be offered. Continuous auditing is significantly different from an annual financial statement audit. A latest research report produced by the CICA defines a continuous audit as: “a methodology that enables independent auditors to provide written assurance on a subject matter using a series of auditors’ reports issued simultaneously with, or a short period of time after, the occurrence of events underlying the subject matter.” However, continuous auditing would present significant technical hurdles. These technical hurdles could be overcome if certain conditions exist. Computer‐assisted audit tools (CAATs) are one of the conditions that must exist in order to conduct the continuous auditing. CAATs are defined as computer‐assisted tools that permit auditors to increase their productivity, as well as that of the audit function. Therefore, with the real‐time accounting and electronic data interchange popularizing, CAATs are becoming even more necessary. The demand for timely and forward‐looking information hints that the continuous audit will eventually replace the traditional audit report on year‐end results.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".