Barriers to environmental management in clusters of small businesses in Brazil and Japan: from a lack of knowledge to a decline in traditional knowledge
Bibliographic record
Abstract
This study aimed to examine the main barriers to environmental management (EM) in two clusters of small businesses (SBs). A study of two clusters was performed: one cluster in Brazil (the leather/shoe sector) and one cluster in Japan (traditional Japanese products). The case studies involved 23 interviews and an analysis of 12 SBs within these clusters. The Japanese cluster has more proactive environmental governance than the Brazilian cluster. The main barrier to environmental improvement in the Brazilian cluster is the lack of information; the main barrier to em in the Japanese cluster is the decline of traditional and environmentally friendly knowledge. The originality of the research is linked to the scarcity of studies of em within clusters and SBs, the comparative approach of the Brazilian and Japanese cases and the discovery of a new barrier to em for SBs (i.e. the decline of traditional knowledge).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".