Disclosure in Annual Reports: An Agency Theoretic Perspective in an International Setting
Bibliographic record
Abstract
Over the recent years 'transparency' or better information disclosure has been the buzzword of various corporate governance bodies due to the failures of the system of corporate governance, which arise from an agency relationship. However, there are arguments by 'free market' advocates suggesting that information is voluntarily disclosed. This study discusses and compares the disclosure of information relating to directors' behaviour in the United Kingdom, Canada, the Netherlands, France, Germany and Sweden. These countries were selected because they represent the developed countries with different accounting systems. Furthermore, they are also the board member countries of IASC and OECD. 'Transparency' relating to directors' behaviour for the six countries were measured using disclosure indices, i.e. the disclosure point average for dichotomous, modified dichotomous weighted dichotomous and weighted modified dichotomous indices. These weighted indices were established by analysing survey responses of investment analysts. The results show that there were significant differences in the disclosure of information relating to directors' behaviour among the six countries. There is also a very low level of 'transparency' in all the countries except the United Kingdom. These results suggest that information relating to directors' behaviour is not voluntarily disclosed and therefore regulation is necessary in order for it to be more transparent as suggested by the various corporate governance bodies.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".