Research on the Competitiveness of Crediting Rating Industry using PCA Method
Bibliographic record
Abstract
Purpose: This study investigates the industry competitiveness problem, which plays an important role in crediting rating industry safety. Based on a comprehensive literature review, we found that there is much room to improve regarding of competitiveness assessment in crediting rating industry. Design/methodology/approach: In this study, we propose the PCA (Principal Component Analysis) method to illustrate the problems. Findings: America and Canada’s companies (such as S&P and DBRS) take the leading place in credit rating industry, and Japan’ agencies have made great progress in industry competition (such as JCR), while China’ agencies are lagging behind (Such as CCXI). Research limitations/implications: It requires multi-year data for analysis, but the empirical analysis is carried out based on one-year data instead of multi-year data. Practical implications: The research can fill the gaps for credit rating industry safety research. And study findings and feasible suggestions are provided for academics and practitioners. Originality/value: This paper puts forward the competitive indicators of credit rating industry, and indicators of cause and outcome are considered.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".