Procedural Factors Influencing Forest Certification Audits: An Empirical Study in Romania
Bibliographic record
Abstract
In the recent decades, forest certification based on third-party external audits has gained momentum. This type of certification has been developed as a monitoring tool aimed at improving governance in corporate environmental management and differentiating products in the increasing environmentally sensitive markets. Although the scholarly literature has extensively analyzed the adoption and dissemination of forest certification, the findings of the external audits and certification practices remain under researched. On the basis of the analysis of 105 audit reports issued by accredited third-party certification bodies in Romania, this article sheds light on procedural factors that have significant influence on the characteristics of non-conformities (NCs) identified by Forest Stewardship Council (FSC) third party audits. Our research offers empirical evidence that certain procedural factors such as the type of assessment, auditing days, number of auditors, or the presence of foreign members in an audit team have a significant influence on the auditing process outcomes: number and grade of non-conformities, standard references, or methods of NC detection. The study opens interesting new lines of research—the influence of procedural or other types of contextual factors on certification outcomes—and provides indications on the effectiveness of the certification procedures and guidelines in certification process quality assurance.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".