Demand for and Impact of Performance Audits on Public Administration in Kazakhstan
Bibliographic record
Abstract
Demand for and impacts of performance audits (PA) on public administration in Kazakhstan have not been studied. This information gap increases risks of missed government opportunities to improve public sector performance. Using new public management theory and the principal-agent model as guiding lenses, the purpose of this phenomenological study was to explore the demand for and effects of PA on Kazakhstan’s public administration. The research question explored the lived experiences and perceptions of the key participants and users of PAs, i.e., auditors, managers of auditees, and parliamentarians, and included reviews of over 200 official documents including audit reports and 14 semistructured interviews. Transcripts underwent inductive descriptive and conceptual coding, integrated application of bracketing and constructed thematic were used to generate and verify themes and patterns associated with PA demand and impact. Research findings illustrated that PAs are frequently requested in Kazakhstan due to problems in public administration, impacting positively and negatively those involved in PA, audit organizations, auditees, and contributing to improved budget process, laws, and regulations. Positive social change implications include providing information for parliamentarian decision-making on using responsive PA and for audit leaders and auditees on development strategies, fostering new performance auditor professional advancement. Additionally, new insights on influential PA triggers may help auditors undertake useful PA, while new PA impact information may support public policy leaders as they steadily improve citizens’ well-being through responsible government.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".