Towards an impartial and effective corporate governance rating system
Bibliographic record
Abstract
Purpose This paper aims to investigate the most popular corporate governance rating systems and to scrutinize their usefulness to shareholders and the public at large. It proposes to examine whether the advertised good governance scores reflect corporate performance, fraud, lawsuits, and the like. Design/methodology/approach The analysis focused on the methodology used by rating agencies to rank corporate governance practices of companies. Analysis of the categories and variables used in the rating systems were also scrutinized and critiqued. Findings This research shows that there is a weak relationship between corporate performance and corporate governance rating. Ideas and suggestions have been proposes to remedy the shortfalls of existing rating systems. Research limitations/implications Many researchers use corporate governance scores in their studies to investigate the relationship between these single scores and corporate performance. Potential vulnerability and risk are demonstrated using such kind of methodologies. Research should be accomplished with the corporate governance indicators separately. Practical implications Several corporate governance ratings systems have been developed and implemented. These systems reduce a complex corporate governance process and related performance into a single score. Such outcome does not in any way reflect the real nature of corporate governance or its performance. Ranking, if it is at all needed, should be interpreted carefully and not be used as a simple measurement of good or bad corporate governance practice. Originality/value This paper is the first of its kind to critically evaluate corporate governance systems scores launched by different rating agencies.
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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 | 0.179 | 0.257 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".