Corporate Social Responsibility Reporting through the Lens of ISO 26000: A Case of Malawian Quoted Companies
Bibliographic record
Abstract
The paper examines the status of corporate social responsibility (CSR) reporting in the annual reports of Malawian quoted companies through the lens of ISO 26000 - Guidance on social responsibility. The study used content analysis methodology. A CSR disclosure index was developed based on ISO 26000’s seven core subjects of social responsibility to measure the level of CSR information disclosure in the annual reports for 2012 and 2013. The results indicate that all the sampled companies were making some CSR disclosures in their annual reports; however the disclosure levels are generally low. Out of the seven CSR themes, the companies scored highly only on organisational governance category and above average on community involvement and development and labour practices categories. On the other hand, they scored poorly on human rights, consumer issues, fair operating practices and environment categories. Furthermore, low individual company scores and the overall score suggest that much more effort is needed to enhance CSR reporting among Malawian quoted companies. The study seems to highlight that there is low awareness amongst the preparers of annual reports regarding all relevant categories that make up CSR. Thus the study recommends promotion of ISO 26000 among them in order to promote holistic approach to CSR that may lead to holistic CSR reporting.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".