Discrepancies in reporting on human rights: A materiality perspective
Bibliographic record
Abstract
Abstract Motivated by the ongoing debate on materiality in environmental, social, and governance (ESG) reporting and the limited attention in the academic literature to date, our study conducts a comprehensive analysis of 1341 ESG reports published by companies across 35 ICB sectors with a particular lens on the commonalities and discrepancies in the choice of material topics on human rights disclosures. The choice of human rights as our topic of interest is driven by the fact that our sample includes companies across various industries/sectors and geographical locations and thus an effective analysis of their reports and the thought‐processes behind them should be done on the basis of topics that apply to a broad range of companies regardless of their country of origin or industry, as well as other systematic and idiosyncratic factors. The reports were examined based on the Global Reporting Initiative (GRI) G4 guidelines to identify a company's disclosure (or lack thereof) on 12 human rights topics. Our analysis of ESG reports comprises the entirety of ESG/sustainability reports contained by GRI's Global Reporting database ( www.globalreporting.org/ ). Our findings suggest that companies diverge considerably in their choices of material human rights disclosure topics. Both industry/sectorial and country/regional factors play an important role on the divergence in materiality assessments. In our further analysis and discussion of the findings, we provide a closer look at the extent to which a consensus has achieved on the disclosure of topics along with potential explanations for the observed divergence in disclosure choices/behavior.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".