Research on the Competitiveness of Crediting Rating Industry using PCA Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it