Social Environmental Disclosure Between Gri-Sustainability Reporting and IIRC – Integrated Reporting Among European Companies
Bibliographic record
Abstract
This critical approach study examines the social and environmental disclosure (SED) between Sustainability Reporting (SR) and Integrated Reporting (IR) among European companies. This paper argues that IR abandons sustainability and might overlap with the functions of SR. The research questions are to examine the integration level of SED within SR and IR and look for the patterns and motifs from reviewing both reports. Applying the critical text analysis method, the GRI G3 guidelines were used to examine a sample of ten European companies. This method is applicable as it does not have rigid procedures to follow (Merkl-Davies et al., 2013). The reports for the selected companies must incorporate fully applied IR without producing any more SR in order to analyse the validity of the data. This study has discovered that there is less integration of SED in IR than SR. The analyses continued by reading and reviewing all reports to identify patterns and motifs. Company strategy and regulatory requirements, reporting style, the crucial issues of the materiality and the development of new sections in the reports were all explored. It is apparent that the IR approach is more towards the primary groups (investors) rather than other stakeholders, society and the environment as a whole. Hence, IR is only a mirror of sustainability for business strategy. Therefore, IR needs to engage reports with other stakeholders to sustain long-term growth.
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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 | 0.050 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".