Bibliographic record
Abstract
Audit’s evidence is basis of auditing. Both the form and the collecting procedure will influence the quality of auditor’s work. Electronic audit’s evidences are increasingly used into practice of auditing as a new kind of evidences. However, auditors are still confused with the dispute of what the definition of electronic evidence is. Hence, the definition of it is given and its natures are discussed. As well as, the approaches of assessing on it are put forth. Key words: Electronic audit’s evidence, Natures of Electronic audit’s evidence, Judgment of electronic audit’s evidence Resume: La preuve de la verification des comptes est le fondement de la verification, la forme et la procedure de rassemblement de la preuve influence directement la qualite de la verification des comptes. En tant que forme nouvelle de la preuve, la preuve de la verification des comptes electronique s’applique de plus en plus dans la pratique de ce domaine. Neanmoins, on n’a pas jusqu’a aujourd’hui une connaissance precise sur ce probleme. Vu cette raison, l’article present presente la definition de la preuve de la verification des comptes electronique, analyse en detail ses caracteristiques specifiques, et propose ses criteres d’evaluation. Mots-cles: preuve de la verification des comptes electronique, caracteristiques, evaluation 摘要：審計證據是審計判斷的依據，審計證據的形式和收集程式直接影響到審計工作品質。電子審計證據作為一種新型的審計證據形式正被越來越多地應用於審計實踐當中，但是對於什麼是電子審計證據這樣的問題，至今未有一個明確的認識。鑒於此，本文闡述了電子審計證據的定義，詳細分析了電子審計證據應具有的特性，提出了電子審計證據的評價標準。 關鍵詞：電子審計證據；電子審計證據的特性；電子審計證據的評價
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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 | 0.054 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".