Theory and Practice of the Post-Audit of Capital Projects
Bibliographic record
Abstract
This paper reports on the post-audit practices of organisations in the Czech Republic. Much of the earlier published empirical research has been conducted in Australia, Canada, the USA, and Western Europe. Only recently, attention has been given to emerging markets, including Eastern European accession/transition countries. This exploratory study's main objective is to identify CZ companies' post-audit practices and obtain opinions on various post-audit issues. It also seeks to assess if these practices mirror those adopted by more developed economies. The research adopts interview, questionnaire, and descriptive statistical analysis approaches. The results present some interesting findings which are of value to both academics and practitioners. Although at the fringe of the general understanding of logistics, project management is a vital part. The authors believe that this is one of the first papers to focus on CZ post-audit practices and fills an important gap in the applied logistics literature.
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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 | 0.032 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.050 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".