Exploring Fallacies and Environmental Responsibilities in the Socio-Environmental Reports of the Brazilian Company Vale S.A.: A Case Study on the Dam Disasters in Mariana and Brumadinho
Bibliographic record
Abstract
The objective of this study was to analyze the level of fallacies present in the socio-environmental reports of Vale S.A., the third-largest mining company in the world, with a focus on the incidents in Mariana (2015) and Brumadinho (2019) in Brazil. We also examined the potential relationship between socio-environmental investments, fallacies, and environmental liabilities during the period of 2010-2022. Fallacies of appeal to motives were extracted from sustainability reports using NVivo®. Data on socio-environmental investments, environmental liabilities, and company disclosures were obtained from Economatica®. Non-parametric statistical analysis using Stata® revealed that socio-environmental investment trends suggested a reduction in environmental liabilities and contingencies. However, this relationship lacked statistical significance. The variable “accident” showed a significant association with investments (p<0.02), indicating a connection between accidents and the company’s investments, impacting environmental liabilities except those related to pre-disaster events. Notably, the company implemented changes in the dam model only after the second accident in 2019, approximately four years after the initial incident. This aligns with the fallacy of appeal to motives, showing a positive and statistically significant association, suggesting an increase in this fallacy after the accidents. In summary, this research analyzed fallacies in the socio-environmental reports of Vale S.A. in the context of the dam failures in Mariana and Brumadinho. It explored the relationship between socio-environmental investments, fallacies, and environmental liabilities, revealing a significant link between accidents and the persistence of certain fallacies despite serious environmental incidents.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".