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Record W1589334687 · doi:10.1002/wcc.329

Adapting forest certification to climate change

2014· article· en· W1589334687 on OpenAlex
Nicole Klenk, Brendon M. H. Larson, Constance L. McDermott

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWiley Interdisciplinary Reviews Climate Change · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsThe Scarborough HospitalUniversity of WaterlooUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsClimate changeCertified woodEnvironmental resource managementForest managementSustainable forest managementVulnerability (computing)Corporate governanceContext (archaeology)Stewardship (theology)CertificationGeographyClimate change mitigationAdaptation (eye)BusinessEnvironmental planningPolitical scienceEcologyEnvironmental scienceForestry

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.036

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.

Opus teacher head0.098
GPT teacher head0.333
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it