CORPORATE REPUTATION AS A CONSEQUENCE OF FINANCIAL REPORTING QUALITY
Bibliographic record
Abstract
Abstract - This paper examines the consequences of Financial Reporting Quality (FRQ) on Corporate Reputation, using three proxies of FRQ: (i) earnings quality; (ii) accounting conservatism; and (iii) accruals quality. The main purpose of this research is to analyze the effect of a good FRQ on corporate reputation measured from Fortune´s annual magazine . To this end, the propounded hypothesis is tested on an unbalanced sample of 186 international non-financial listed companies from 930 observations from United Kingdom, United States and Canada for the period 2006-2010. The use of simultaneous equations for panel data, via the GMM estimator propounded by Arellano and Bond (1991), highlights the positive effect of FRQ (earnings quality, conservatism and accruals quality) on Corporate Reputation. Shareholders, market, investors, society and other stakeholders assess positively those companies that report financial information of better quality. This result is robust according to the different measurements of FRQ.
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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 | 0.007 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".