Adapting forest certification to climate change
Bibliographic record
Abstract
In the context of climate change, the forest sector must consider the extent to which sustainable forest management enables or constrains climate change adaptation and mitigation; it may be that existing values and principles, policies and decision‐making processes, and institutions are no longer appropriate. Forest certification has emerged as an important arena for setting international and regional standards for forest management, but it is unclear to what extent it supports or helps develop adaptive capacity for climate change in the forest sector. This paper, therefore, combines a review of the literature on forests and climate adaptation with a systematic assessment of the Forest Stewardship Council Criteria and Indicators (in detail) and other forest and carbon certification schemes (in brief) to shed light on the role of certification standards in mediating forest and climate adaptation strategies. WIREs Clim Change 2015, 6:189–201. doi: 10.1002/wcc.329 This article is categorized under: Climate, Ecology, and Conservation > Conservation Strategies Vulnerability and Adaptation to Climate Change > Institutions for Adaptation Policy and Governance > Multilevel and Transnational Climate Change Governance
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".