Tamed Transparency: How Information Disclosure under the Global Reporting Initiative Fails to Empower
Bibliographic record
Abstract
In this contribution, we explore the tensions that seem inherent in the claim that transparency policies “empower” the users of disclosed information vis-àvis those who are asked to provide the information. Since these tensions are particularly relevant in relation to voluntary disclosure, our analysis focuses on the Global Reporting Initiative (GRI) as the world's leading voluntary corporate non-financial reporting scheme. Corporate sustainability reporting is often hailed as a powerful instrument to improve the environmental performance of business and to empower societal groups, including consumers, in their relations with the corporate world. Yet, our analysis illustrates that the relationship between transparency and empowerment is conflictual at all four levels of activity examined in this article: in the rhetoric and policies of the GRI as well as in the actual reporting practice and in the activities of intermediaries in response to the organization's disclosure standard.
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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 | 0.071 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.058 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".