Analysis of Sustainability Reports and Quality of Information Disclosed of Top Brazilian Companies
Bibliographic record
Abstract
The objective of this research is to examine the quality of information disclosed from a sample of Brazilian listedcompanies, using a multidimensional construct based on economic, environmental and social dimensions ofsustainability. The research design combines both quantitative and qualitative methods. The qualitative approachis used in the content analysis procedure and the quantitative is employed for statistical analysis. The targetpopulation consists of top 36 sustainable companies (ISE) and 24 with corporate governance practices (NM) in2011. We find that 37% of the companies achieved score above 0.5; 30% between 0.26 and 0.5 and 33% scoredbelow 0.25, being score zero the worst and one the best score. The best company scored 0.896 and the worst ofthe 60 companies scored 0.0167. Overall our statistical results confirm that ISE companies tend to disclose moreinformation and in a more adequate way than NM, and in general, the companies are reporting the content in allthe three dimensions with same quality level. Furthermore, companies from Infrastructure sector present betterquality content reported when compared to Service companies. We conclude that a good sustainability report isdirectly related to the good content in all the tree dimensions, regardless the economic sector and these reportsstill have a big room for improvement, which echoes within the literature analyzed. Companies need to disclosetheir information in a more integrated way, addressing sustainability issues under the scope of business strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".