Wood procurement policy: An analysis of critical issues and stakeholders
Bibliographic record
Abstract
During the last two decades, the ecological, cultural and social values of forests have received stronger priority by society. To address the changes in values in a forest products context, major wood and non-wood retailers are being asked to develop a wood procurement policy which defines the sources from which a company or organization will or will not obtain the wood or wood products it requires. Many interest groups are actively advising companies and organizations that are currently developing wood procurement policies, and continue to urge other retailers to commit to developing such policies. However, there is a welter of inconsistent and confusing wood procurement policy terminology that has been created, and is unlikely to successfully advance the cause of sustainable forest management in the medium and long term, at least in North America. In many cases, the broader objectives of the policies are not clear and this, as well as the lack of discussion between all relevant parties, is likely to create difficulties in policy implementation and in consumer acceptance. This paper describes and analyzes the current and emerging stakeholders and the processes that are necessary for successful wood procurement policy implementation – setting objectives, developing terms and definitions, identifying indicators, linking with data available and verifying data. Particular attention is given to exploring the problems with existing terms and definitions. We conclude that definitions and data collection standards need clarification, regional differences in forests need to be recognized, methods for data validation developed, and target deadlines for full implementation of a wood procurement policy possibly extended. The next steps could be a series of meetings between key stakeholders, including the wood and non-wood industries, forest products industry, certifying and standard-setting bodies, and ENGOs. These meetings are needed to advance the discussion on definitions, standards, and data to use with the goal of effectively connecting wood procurement policy with sustainable forest management. Key words: policy, forest, wood procurement, definitions
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".