Positive Accounting Information for Sustainable Development Decision-making Using Sustainability Financial Monetising Accounting System: An Empirical Study
Bibliographic record
Abstract
Companies are faced with growing concerns and pressures from stakeholders and the public regarding their corporate social responsibility (CSR) performance and its impacts upon the environment and society in which they operate. Despite the spectacular increase in the application of different sustainable accounting systems to meet sustainability objectives, companies are still unable to provide stakeholders with satisfactory, clear and reliable monetary measures based on accounting determinates indicating their sustainability performance. This motivates the study to introduce and test the Sustainability Financial Monetising Accounting System (SFMAS) that is developed in this study to provide sustainable accounting practices with reliable, accurate and clear CSR identification and monetary measurements for organisations concerned with being sustainable and applying sustainability accounting concepts. The results of the quantitative data analyses that employ industrial and service listed companies from Australia, Canada and the United Kingdom support the development of a SFMAS conceptual model mechanism. The new mechanism is built upon free cash flow as an integrated comprehensive accounting measure from stakeholders' perspective. The SFMAS supports stakeholders' interests while developing internal management decisions and external reporting and disclosure, by employing a more accurate and reliable monetary accounting information system. By applying the SFMAS, companies could create value though economic, corporate governance, social, and environmental performance, and, therefore, would become environmentally and socially aware organisations in the eyes of stakeholders and the marketplace.
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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 | 0.010 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".