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Record W2528831442 · doi:10.1505/146554816820127569

Bundling forest ecosystem services for FSC certification: an analysis of stakeholder adaptability

2016· article· en· W2528831442 on OpenAlex
Wanggi Jaung, Gary Bull, L. Putzel, Robert Kozak, Chris Elliott

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.

Bibliographic record

VenueThe International Forestry Review · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of British Columbia
FundersConsortium of International Agricultural Research CentersWWF InternationalWorld Wildlife Fund
KeywordsAdaptabilityCertificationStakeholderEnvironmental resource managementBusinessEcosystem servicesEcosystemForest ecologyEcologyEnvironmental scienceEconomicsBiologyManagement

Abstract

fetched live from OpenAlex

An expansion of Forest Stewardship Council (FSC) certification to forest ecosystem services (FES) is a potential tool to improve FES management. Certification of FES in bundles is an expected strategy because it could decrease trade-offs among FES, increase forest owners' incomes, and reduce certification costs per FES. However, there is insufficient evidence of which bundles FES would be most feasible to certify. This study assesses the adaptability of the FSC system to FES bundles through analyses of FES projects and surveys of FSC certification bodies, enabling partners, and certificate holders. Exploratory factor analysis and multiple correspondence analysis identified two bundles: 1) soil and watershed conservation and 2) cultural ecotourism with non-timber forest products or agricultural goods. These findings indicate potentially manageable FES bundles, given the current FSC system and FES projects, as well as some implementation challenges.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.658
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.072
GPT teacher head0.306
Teacher spread0.234 · 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