Improving ESG Scores with Sustainability Concepts
Bibliographic record
Abstract
ESG (environment, social, and governance) scores are becoming mainstream proxies for evaluating sustainability in organizations. In past years, scholars and managers used ESG scores to express the sustainable development of an organization and other types of sustainability. Meanwhile, increasing literature has shown that ESG scores do not measure sustainability in terms of sustainable development. The main reason ESG scores fail to measure sustainability adequately is that ESG scores are not designed to measure sustainability concepts, such as temporality, impact, resources management, and interconnectivity. Furthermore, ESG scores apply materiality concepts, but what they measure is not always quantifiable, and most agencies that produce ESG scores lack transparency. This research reviewed the challenges and issues associated with ESG scores regarding sustainability representation. Then, based on the sustainability literature, different themes and concepts that would add more sustainability consideration to an ideal ESG score are presented. Since ESG scores are increasingly popular, this paper presents concepts and ideas that would help ESG score agencies include more sustainability principles in their methodologies while redefining the expectations of scholars using them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".