Compliance with the Corporate Governance Code: are there any improvements
Bibliographic record
Abstract
The first corporate governance code in its present-day meaning – the Cadbury Code – was adopted in the UK in 1992 when the Cadbury Committee on Corporate Governance Issues developed the guidelines for the best corporate governance practices. The Cadbury Code laid the foundation not only for British corporate governance codes, but also paved the way for development of such codes in Europe. Late in the 1990s and early in the 2000s, corporate governance codes were approved in Austria, Belgium, Germany, France, Switzerland and Sweden.[1] At the same period, similar documents were developed in Australia, Canada, the USA and Japan. In Russia, the first corporate governance code was adopted in 2002. At present, a majority of developing and developed countries have introduced such codes, too.
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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 | 0.073 | 0.280 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".