Information Asymmetry,the Characteristics of Directorate and the Selection of Management Forecast Disclosure——An Empirical Study Based on the Data From 2004-2007 of a Listed Company in China
Bibliographic record
Abstract
We use the mixed data from the 1st quarter of 2004 to the 4th quarter of 2007 of a listed company in China to test the relation among information asymmetry degree,the characteristics of directorate and the selection of management forecast.It finds that:(1) with directorate size expanding and meeting frequency increasing,the precision of disclosure manner decreases and the disclosure is not in time.With the proportion of independent director increasing,the precision of disclosure manner increases;(2)if we take the interaction with information asymmetry degree into account,the degree of negative correlation between directorate meeting frequency and the precision of disclosure manner,and the degree of positive correlation between the proportion of independent director and the precision of disclosure manner both increase with the increase of information asymmetry degree.The effect that directorate size acts on forecast bias and the proportion of independent director acts on forecast bias will be different with the change of information asymmetry degree.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".