Bibliographic record
Abstract
Capital structure, pension, credit rating, and CSR performance has been broadly discussed in the field of corporate finance. To research and discuss the questions of the impacts and relations between capital structure, pension, credit rating and CSR performance, this paper tries to find the answer through two dimensions: the firm side and the executives' side. Based on the positivist approaches delivered by past studies, this paper will be conducted under a positivist paradigm. Gender, age and salary are the three significant executive characteristics determining firm structure on top of profitability, liquidity and asset tangibility. Further, in the researched time period and selected companies, the pension plan is a substitute for external debt. From the result of the CSR-linked compensation model, again profitability, asset tangibility, liquidity, executive's salary, and executive's total compensation are the variables that are significant. Through the uni-variate analysis, multivariate analysis and building models to test and fit different dependent, at the same time testing the multi-collinearity and endogeneity we found that although theories of capital structure, cost of capital, agency problems and stakeholder expectations have contradictions and are inconsistent, they fill up the gap between each other and verify the relationships between the firm characteristics and executive's characteristics in the field of corporate finance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".