Forest management certification in the Americas: difficulties in complying with the requirements of the FSC system
Bibliographic record
Abstract
'Forest management' aims to maintain forests as producers of goods and services, while ensuring their conservation for future generations. Forest certification has become one of the most widely used mechanisms to encourage and recognize this 'forest stewardship', with the Forest Stewardship Council (FSC) among the most well-known systems worldwide. FSC is widely used in several Management Units on the American Continent, which is home to large forest areas. Therefore, we evaluated the main difficulties in complying with the principles of the FSC standard in 18 American countries based on verification of non-conformities generated in the process. The data were obtained from information contained in the certification audit reports available on the FSC official website, covering all organizations with valid certificates from 1995 to 2013. We found that the United States presented the lowest mean of non-conformities per audit, which may indicate better capacity of managers to implement practices of its forestry activities. Regarding the deviation type, the United States and Canada presented higher indices in relation to the adequacy of the environmental impacts (P6) of their activities. Meanwhile, the greatest non-conformities in the Central and South America countries occurred in the labor and social area (P4), followed by environmental issues (P6). All organizations presented some type of non-compliance with the criteria set by the FSC and needed to adapt. The major difficulties encountered were related to compliance with environmental requirements. The need to implement corrective actions to maintain the certificate demonstrates a change of management influenced by the forest certification process, thus contributing to minimizing socio-environmental impacts resulting from forest operations.
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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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".