Bibliographic record
Abstract
On the base of analysis of teaching of audit practice and the request of audit talent in China, some points are put forth, namely, how to improve audit practice teaching and variety of teaching medium. Key words: Audit; Practice teaching; Teaching quality Resume: Sur la base de l'analyse de l'enseignement des pratiques d'audit et de la demande des auditeurs en Chine, certains points sont mis en avant pour expliquer comment ameliorer l'enseignement des pratiques d'audit et la variete des moyens de l'enseignement. Mots-cles: Audit; Pratique de l’enseignement; Qualite de l’enseignement 摘要：本文通過分析審計實踐教學的現狀，結合目前我國對審計人才的要求，提出要提高審計實踐教學品質這 一觀點，並闡述了改善審計實踐教學的幾點措施。 關鍵詞：審計；實踐教學；教學品質
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".