Bibliographic record
Abstract
本文考察上市公司与外部资本市场之间的信息不对称对集团母子公司之间债务分布决策的影响,以及会计稳健性在债务主体选择决策中发挥的信息调节效应,研究发现:当信息不对称程度较低时,子公司会更多地选择自主承担债务融资,但随着信息不对称程度的提高,债务融资则更多集中于母公司,表明信息不对称降低了子公司自主承担债务融资的能力,上述倾向在子公司业务比重大的企业集团中表现得更为明显。进一步地,条件稳健性显著缓解了信息不对称对子公司自主承担债务融资的约束,而非条件稳健性却加剧了上述约束。研究揭示了信息不对称除影响公司信贷总体水平外,还是影响集团母子公司之间债务融资主体选择的重要因素,并且两类会计稳健性在集团信贷主体决策中存在信息效应异质性,因此深化了会计稳健性与信贷决策关系的研究。
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".