Measuring sustainability risks: A rational myth?
Bibliographic record
Abstract
Abstract The objective of this paper is to investigate the rigorousness and reliability of sustainability rating agencies' evaluation of corporate sustainability risks. Using grounded theory, this study conducts a qualitative analysis of 32 semi‐structured interviews with practitioners involved in this activity and shows the trade‐offs and rational myths underlying this evaluation process. The image of rationality and rigorousness projected by sustainability risk measurements is mostly intended to address the increasing institutional pressures for reliable and comparable information, particularly from institutional investors and socially responsible investment decision makers. Nevertheless, risk analysts face serious challenges due to the lack of reliable information, the unpredictability of sustainability risks, the methodological issues related to the measurement process, and the complexity and context‐dependency of risk assessment. These challenges call into question the official and optimistic rhetoric of rating agencies. This study contributes to the literature on sustainability risks and rational myths in organizations. Managerial implications and avenues for future research are also discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".