A framework for assessing the effectiveness of forest certification
Bibliographic record
Abstract
With increasing concerns about the costs of forest management, there is a need to rigorously evaluate any management activities that add to costs. Certification has been widely adopted at considerable financial cost to those managing forests. Although there have been many studies of the impacts of certification, there is no comprehensive framework for assessing whether or not certification has been effective in achieving its goals. To do this, certification needs to be viewed as a part of an international environmental regime. Using established methodologies, this paper applies an evaluation framework and examines forest certification effectiveness in a number of categories: problem solving, goal attainment, behavioural effectiveness, process effectiveness, constitutive effectiveness, and evaluative effectiveness. It is too early to assess its effectiveness in problem solving and goal attainment. However, forest certification has been quite successful at process and constitutive effectiveness and is now widely recognized by a range of institutions. Its effectiveness in changing behaviours is less clear, and its evaluative effectiveness remains to be determined.
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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 | 0.096 | 0.137 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.008 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".