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Record W2319950294 · doi:10.5558/tfc2012-109

Forest certification in Asia: The changing marketplace for value-added wood product manufacturers in China and Vietnam

2012· article· en· W2319950294 on OpenAlexvenueno aff
Tait Bowers, Ivan Eastin, Indroneil Ganguly, Jeff Cao, Mihyun Seol

Bibliographic record

VenueThe Forestry Chronicle · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsnot available
Fundersnot available
KeywordsCertificationBusinessCertified woodProduct (mathematics)ChinaMarketingForest productForest managementForestryEconomicsGeography

Abstract

fetched live from OpenAlex

China and Vietnam have become two of the key wood manufacturers in Asia that export a large percentage of value-added wood products globally. As regulations and environmental awareness from countries that import these products increase, manufacturers have had to establish certification practices for continued accessibility to these markets. A study based on managerial interviews and a survey with over 800 wood product manufacturing operations in China and Vietnam asked questions on how forest certification had been implemented in their business practices and what challenge had come from the adoption of these standards. These responses were analyzed to determine the contributing factors to the commitment to certification. Comparisons between China and Vietnam were used to identify country-specific factors that may influence a company's decision to obtain forest certification for its operations. Survey respondents indicated that acquiring certified raw material supply, market awareness, and certification costs were found to be the major constraints encountered in adding certified products to their sales mix. Lack of a domestic supply of certified wood was also a problem that led to a heavy reliance on imported wood at additional costs to the bottom line. Results revealed significant differences among the two countries regarding their perceptions of the benefits of certification, but showed that market benefits were the leading driver to a company's commitment to certification.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.423

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2012
Admission routes1
Has abstractyes

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