Governance in the Public Sector Internal Audit: A Dynamic Comparative Perspective
Bibliographic record
Abstract
The 21st century was marked by the "internal audit explosion" in all sectors of the global economy. However, very little is known about its origins, characteristics, scale and impact on public sectors. This thesis advances the emerging and poorly understood field of public-sector internal auditing (IA) by providing the first comprehensive examination of the public-sector reforms that led to IA explosion from the dynamic and comparative analytical perspectives.The thesis argues that public-sector IA explosion is a manifestation of a paradigmatic shift in the global IA profession, led by the global Institute of Internal Auditors (IIA), and dubbed by the author as a "Value-Added Management Partner Paradigm" (VAMP). The methodology is based on the author's conceptual framework for the analysis of IA policy dynamics and variation, based on adaptation of Howlett and colleagues (2009) meta-theory of policy making as an over-arching research philosophy, and incorporating a range of mid-level theories, frameworks, and concepts from three fields of knowledge: public policy, business administration, and IA profession.The study produced several important findings. First, the source of VAMP genesis - the modern IA industry - went through three professional paradigms during 1941-2017, the last of which became a global best-practice blueprint for the IA activity and IA reforms. Second, VAMP was strongly recognized and advocated by the key supra-national institutions, because it fit with their international regimes and agendas. Third, there is strong evidence that over the last two decades a global adoption of the VAMP model of IA activity occurred in the public sectors across the world, leading to a high degree of convergence in the IA practices. Fourth, case studies on IA reforms in the national Westminster governments of the UK, Australia, and Canada as early adopters of VAMP, demonstrated that while the VAMP has taken the firm ground in the IA sectors there, the countries vary with respect to the established legislative and normative base and design of the IA governance regimes.The major implication is that VAMP-inspired public-sector IA reforms are still work-in-progress and their outcomes are difficult to assess.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".