Editorial: Governance, risks and rules between theoretical studies and empirical analyses
Bibliographic record
Abstract
The first issue of the journal in 2020 (volume 10, issue 1) provides a careful analysis of the important field of research regarding the social indicators, the corporate governance system, risk analysis and risk management, disclosure and bank regulation. Specifically, the current issue pays attention of an index to measure the quality of the most important European cities, the evolution of Saudi Arabia corporate governance systems, the econometric approach to estimate the influence of interest rates and inflation rates on default rates of banks, the Canadian companies and risks firms disclose, the relevance of enterprise risk management (ERM) information disclosure in the US banking sector and the bank regulation of capital and risk management in the Europe and Central Asia region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.016 | 0.016 |
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".