Internal Control Assessment and Interference Effects
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 Both U.S. Generally Accepted Auditing Standards and International Standards on Auditing require risk-based audits, where audit effort is concentrated on accounts and financial statement assertions where the risk of material misstatement is high. Assessing risk requires the auditor to evaluate the auditee's internal control systems; however, current standards and practice vary regarding the point at which risks are to be identified. Using output interference theory, we hypothesize that risk assessment performed by the auditor before evaluating the client's internal control systems will lead to a more complete identification of sources of internal control deficiencies as compared to assessing risk after evaluating internal control systems. In our experiment, auditors who identified risks first identified more, and more important, internal control deficiencies than did auditors identifying controls first, although the number of risks identified was not significantly different between the two groups. Overall, our results suggest that audit efficiency and effectiveness depend on the sequence in which internal control evaluation subtasks are performed. Data Availability: Data are available from the authors upon request.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it