Alignment Vetting of Bloomberg’s ISS: QualityScore [GQS]: Frequency of Provision of ESG & Related Disclosure Scores
Bibliographic record
Abstract
Context The Environment, Social, and Governance [ESGÓ]-platform offered by BloombergÔ Professional Services [https://www.bloomberg.com/professional/] is a leading source of relevant, reliable, and timely information on the context within which market trading firms operate. The ESG-platform of the Bloomberg Terminals [BBT] includes more than 2,000 data fields that provide intel to aid in better understanding the “Stakeholder-impact” of the firm’s activities. One of the sub-platforms therein is the Institutional Shareholder Services [ISS] which offers Governance QualityScores: (GQSÔ). The BBT[ISS[GQS]]-platform is a data-driven approach to scoring & screening designed to help investors monitor a company’s control of governance risk. Previous studies have provided vetting information of the BBT[ISS[GQS]]-platform. As an enhancement to these vetting-studies, we offer the following. Study Design In the ESG-Platform, there are Disclosure Scores for: The General [ESG], Environment, Social & Governance categories. The vetting question of interest is: Does the ISS score those firms that provide more Disclosure information as ISS[1] and those firms that provide less as ISS[10]? If so, this would cast doubt on the relevance and reliability of the ISS-assignment taxonomy. Results We discuss the critical role of vetting. Then, the Dul: Necessity & Sufficiency Screen is offered as the organizing logic of the Inferential vetting platform. Finally, using the Gold Standard test: Linear Discriminant Analysis for the vetting inference, it is clear that the ISS-assignment is not aligned with the degree of provision of disclosure information for any of the four ESG-Disclosure Score variables. Thus, these vetting results are not inconsistent with a functioning taxonomic-allocation platform.
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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 | 0.039 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".