MAPEAMENTO DA PESQUISA SOBRE DISCLOSURE AMBIENTAL NO CENÁRIO INTERNACIONAL: UMA REVISÃO DOS ARTIGOS PUBLICADOS EM PERIÓDICOS DE LÍNGUA INGLESA NO PERÍODO DE 1997 a 2007
Bibliographic record
Abstract
The objective of this paper is to present a literature review on environmental disclosure. The increasing concern of society and the scarceness of works regarding this theme in Brazil are the main motivations for conducting this research. Our work has been conducted from a sample of 52 accounting and auditing journals, published in English and available at the Capes’ Basis. A total of 77 papers published during the period of 1997-2007 have been selected. Findings evidence that the great majority of papers have been conducted in the United Kingdom, Canada, Australia, New Zealand and in the United States. Regarding the type of study, 10 papers are theoretical, 67 empirical studies. Out of the empirical ones, 54 are survey or quasi-experimental, 11 are case studies and 2 are experiments. The majority of studies used secondary data, and content analysis has been utilized in several studies in order to analyze the environmental disclosure. Authorship analyzes has evidence that Denis Paten, from Illinois State University, is the most prolific author of the area. Papers also evidence that disclosure varies across countries and sectors, and researches have showed concern regarding the need for a standard and audit of environmental information. Finally, this papers wishes to contribute to the construction and to the improvement of Brazilian accounting research, as there is a lack of studies addressing this subject in the national scenario.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.039 | 0.068 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".