Internal auditing around the World. A Perspective on global regions
Bibliographic record
Abstract
The Institute of Internal Auditors’ (II A’s) Global Internal Audit Common Body of Knowledge (CBOK) Survey is conducted every few years and the results help to identify the continuous development of the profession of internal auditing around the world. The survey also highlights the different practices, attributes, and internal and external factors (such as social, political, environmental, and economic) that are shaping the worldwide profession. \n \nThis research report provides an in-depth analysis of the factors and rationale behind the development of the profession and seeks to identify opportunities for the transfer of success stories to the rest of the internal audit community. To perform the analysis, the researchers initially analyzed the 2010 CBOK results, comparing the data for each region. Where the 2010 and 2006 survey questions were comparable, a temporal comparison was conducted to analyze those trends. \n \nAs explained in chapter 1, our in-depth analysis is based on interviews with members of the internal audit profession as well as chairs and members of audit committees in seven geographical areas: Africa, Asia-Pacific, Eastern Europe and Central Asia, Latin Americaand Canada, and Western Europe.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".