Editorial: From cross-country to multi-disciplinary research in corporate governance
Bibliographic record
Abstract
The recent issue of the journal has been composed of the papers which are mostly empirical and contribute new ideas to the major issues of corporate governance such as board of directors, chief executive officer (CEO) pay, shareholder activism, accounting, auditing, social responsibility, family firms, firm performance, social capital in corporate governance, etc. We are pleased to inform you that scholars from many countries of the world are authors of these papers. They represent the USA, Canada, Germany, Italy, Switzerland, New Zealand, Hong Kong, India, Tunisia, etc. This makes the recent issue of the journal very interesting for the readers. These papers provide a solid contribution to the previous research by Abbadi, Abuaddous, and Alwashah (2021), Kostyuk, Mozghovyi, and Govorun (2018), Cranmer (2017), Santen and Donker (2009), Guerra, Fischmann, and Machado Filho (2008).
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".