Sustainability rating and moral fictionalism: opening the black box of nonfinancial agencies
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the practices, challenges and ethical issues underlying the fabric and dissemination of corporate sustainability ratings. Design/methodology/approach Based on 36 semi-structured interviews with sustainability rating practitioners, the study shows the trade-offs, ethical judgments and customizable aspects involved in rating practices, which cannot rely only on formal and predefined methods. Findings In contrast with the official optimistic rhetoric about the rationality and rigor of sustainability rating methods, agencies face serious challenges in the measurement and comparison of performance in this area, particularly in terms of the aggregation of scattered and fuzzy indicators, commercial pressures and the availability, materiality and reliability of the information collected. Despite these concerns, sustainability ratings do appear to be useful in improving corporate responsiveness and increasing investor awareness of the complex and difficult-to-measure aspects of nonfinancial reports. Practical implications Rating agencies should collaborate to set up common indicators that would be easier for firms to produce and should better separate their sustainability rating production activities from other services they offer to companies (e.g. consultancy). Originality/value This study contributes to the literature on the measurement and promotion of corporate sustainability by analyzing rating practices through the lens of moral fictionalism, which here refers to the human tendency to build ethical judgments on fictional but convenient and useful representations.
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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 | 0.176 | 0.377 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.062 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| 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".