Sustainability reports as simulacra? A counter-account of A and A+ GRI reports
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the extent to which sustainability reporting can be viewed as a simulacrum used to camouflage real sustainable-development problems and project an idealized view of the firms' situations. Design/methodology/approach – The method was based on the content analysis and counter accounting of 23 sustainability reports from firms in the energy and mining sectors which had received application levels of A or A+ from the Global Reporting Initiative (GRI). The information disclosed in some 2,700 pages of reports was structured around 92 GRI indicators and compared with 116 significant news events that clearly addressed the responsibility of these firms in sustainable development problems. Moreover, the 1,258 pictures included in sustainability reports were categorized into recurring themes from an inductive perspective. Findings – A total of 90 per cent of the significant negative events were not reported, contrary to the principles of balance, completeness and transparency of GRI reports. Moreover, the pictures included in these reports showcase various simulacra clearly disconnected with the impact of business activities. Originality/value – The paper shows the relevance of the counter accounting approach in assessing the quality of sustainability reports and question the reliability of the GRI's A or A+ application levels. It contributes to debates concerning the transparency of sustainability reports in light of Debord's and Baudrillard's critical perspective. The paper reveals the underexplored role of images in the emergence of several types of simulacra.
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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 | 0.029 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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 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".