Disclosure of Risks and Opportunities in the Integrated Reports of South African Banks
Bibliographic record
Abstract
This study examined the disclosure of risks and opportunities in the integrated reports (IRs) of the top five banks in South Africa. It assesses whether the risk and opportunity disclosures provided comply with the requirements of the International Integrated Reporting Framework (IIRF), as well as the nature of the risks and opportunities disclosed in the IR. This study takes a qualitative approach and employs an interpretivist paradigm. The information for this study was obtained through content analysis of the individual banks’ latest available IRs. A checklist was created as a measuring tool to evaluate disclosure practices. The findings showed that three of the selected banks disclosed all the requirements contained in the IIRF regarding risks and opportunities, while two banks only partially complied as they did not provide disclosures about their opportunities. The findings concerning the nature of risk disclosures show that the selected banks disclosed 38 themes related to risks, and the findings concerning the nature of opportunity disclosures show that the selected banks disclosed 14 themes related to opportunities. Furthermore, the results show that those in charge of preparing the IRs provide a thorough disclosure of risks, while there is room for improvement concerning disclosure of opportunities.
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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 | 0.016 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".