A Content Analysis of Auditors' Reports on it Internal Control Weaknesses: The Comparative Advantages of an Automated Approach to Control Weakness Identification
Bibliographic record
Abstract
We employ an automated content analysis approach to provide a snapshot of the terminology auditors actually use to describe information technology weaknesses (ITWs). We develop and use a dictionary based on textual analysis of auditors' reports on internal control filed under Section 404 of the Sarbanes–Oxley Act from 2004 to 2009. Using the dictionary with content analysis software led to the identification of 14 categories of ITWs in order of decreasing frequency of occurrence: (1) access, (2) monitoring, (3) design issues, (4) change and development, (5) end-user computing, (6) segregation of incompatible functions, (7) policies, (8) documentation, (9) masterfiles, (10) backup, (11) staffing sufficiency and competency, (12) security (other than over access), (13) outsourcing and (14) operations. The use of automated content analysis methodology also helped us identify potential disconnects between terminology used in auditors' reports and that used in published frameworks and guidelines. We provide the dictionary and discuss the methodology used in creating and applying the dictionary to the analysis of the textual content of auditors' reports on internal control, including the advantages and limitations of automated ITW identification.
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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 | 0.018 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.030 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".