Bibliographic record
Abstract
在上一期专栏文章中,我们从性别与道德、风险厌恶以及性别多样性角度三方面阐释了董事会中的性别多元化对欺诈频率和严重程度有哪些影响,并且我们还区分了这些因素在男性主导产业与女性主导产业中的不同表现。我们得出了三个假设:公司董事会中的女性成员减少了欺诈的频率,而在董事会中女性比例最高为50%的公司中,这种影响正在减弱;董事会的性别多样性减少了投资者对欺诈声明的负面反应;女性董事在男性主导行业的影响将强于女性主导的行业。 本文根据 Douglas Cumming，T.Y. Leung，Oliver M. Rui. Gender Diversity and Securities Fraud. Academy of Management Journal, 2015, 1572-1593. 论文翻译而成。刘心洁编译，论文原作者之一芮萌教授对原文进行了适当改写。
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".