Reporting of Real Option Value Related to ESG: Including Complementary Systems for Disclosure Incentives
Bibliographic record
Abstract
This paper aims to consider ways of granting disclosure incentives in order for the Signaling Theory to develop and encompass the Legitimacy Theory. First, the author discusses that ESG strategies for managing stakeholder externalities create real option value that leads to corporate value creation, both as business opportunities as well as appeals to a company’s legitimacy. At the same time as making real option thinking useful for strategic decision-making by management, it is necessary to structure non-financial information disclosure for convincing optionality related to controlling externalities from the viewpoint of investors.Second, at the stage where conditions are not met for companies able to autonomously undertake management with a view to externalities, the author discusses how supplementing incentives for issuing signals regarding differentiation from other companies in the same industry relating to controlling externalities is required in the disclosure of non-financial information in statutory reporting systems. On the other hand, since the materiality of financial reporting is centered on risks and opportunities for business, disclosure regulations are required separately for material social values. Events not originally related to corporate value can create incentive for the fulfillment of accountability of companies by the mediation of “negative intangibles” through reputation.
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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 | 0.028 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| 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".