Forest certification: a policy perspective
Bibliographic record
Abstract
This paper analyses the development of certification programmes in three countries (Indonesia, Canada and Sweden) using the Advocacy Coalition Framework (ACF) as a theoretical reference point. The ACF is an actor-based framework for analysing policy processes and has not previously been applied in a developing country. Actors in the three countries took different approaches to certification. In Canada, in a programme development process supported by the forest products industry, a management systems approach was taken. In Sweden, performance standards were developed in a process initially driven by NGOs. In Indonesia, certification was led by an NGO within a framework established by government, and a performance standards approach was used. The paper concludes that forest certification can be best understood as a policy instrument that promotes and facilitates policy-orientated learning among actors, and provides indirect incentives for improved forest management. Learning occurs both as the standards to be used for certification are developed, and as they are implemented. The benefits of learning and consensus building among actors (such as NGOs, forest companies, private forest owners, indigenous peoples, governments, etc.) who have traditionally been in conflict with each other can be significant. On the other hand, where fundamental changes in forest policy (such as tenure and forest revenue reform) are needed, certification should not be seen as a substitute for these A further conclusion is that, while public policies change over periods of decades, the private policies of retailers and forest product companies can adapt more rapidly to changing circumstances. The concept of a ‘fast track’ of private policy change, compared to the slower track of governmental policy change, is therefore proposed and described. A number of interesting theoretical and empirical avenues for further research on certification are discussed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".