Variáveis Institucionais e o disclosure socioambiental das empresas que atuam no setor de papel e celulose
Bibliographic record
Abstract
Corporate Social Responsibility has gained greater importance over time. Existing literature points to evidence that the institutional characteristics of each country exert influence on the social and environmental disclosures of organizations. This research aims to verify the effects of certain institutional variables in the social responsibility reports of companies operating in the pulp and paper sector, a segment with great economic importance globally that receives significant pressure from various stakeholders to act responsibly, both socially and environmentally. Several variables were considered, including the Gross Domestic Product, Legal System, Worldwide Governance Indicators, Human Development Index, and the company's size. Reports for twenty-seven organizations operating in Brazil, Canada, the United States, Japan, and Sweden between 2015 and 2018, were analyzed. Descriptive statistics showed that the most publicized category by the organizations observed is environmental, and the least belongs to the group of aspects related to human resources. The results showed that a country's GDP positively impacts economic and environmental disclosures. Having a common-law legal system negatively influences economic disclosures, as well as product and consumer disclosures. Governance indicators, on the other hand, exert positive effects on information related to human resources. The HDI only has a negative relationship with the group of social variables, contrary to the study's expectations. The findings allow us to conclude that, although the sector of the analyzed companies exerts a determining effect on the choice of the most evident category in the reports, it is possible to affirm the existence of a relation of impact, sometimes positive, sometimes negative, of certain institutional variables on the socio-environmental disclosure of organizations.
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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 | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".