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Record W2133637763 · doi:10.5558/tfc2014-130

The impact of timber legality regulations on business practices in Vietnam

2014· article· en· W2133637763 on OpenAlexvenueaboutno aff
Benjamin Roe, Ivan Eastin, Indroneil Ganguly

Bibliographic record

VenueThe Forestry Chronicle · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
FundersU.S. Department of Agriculture
KeywordsPrinciple of legalityBusinessChinaVietnameseCertificationSupply chainIllegal loggingCertified woodEuropean unionCommerceInternational tradeAgricultural economicsLoggingGeographyForestryLawMarketingEconomics

Abstract

fetched live from OpenAlex

Reports that nearly half of Vietnam's roundwood imports come from illegal sources has drawn criticism from major consumer countries who have recently implemented timber legality regulations. These regulations, which include the U.S. Lacey Act and EU Timber Regulation, restrict the import of illegally harvested wood and are expected to have a direct impact on major wood-processing countries such as Vietnam and China. Surveys were conducted at trade shows in Ho Chi Minh City to assess how these regulations influence firms’ use of chain-of-custody certification, and impact their material sourcing and export market decisions. Vietnamese firms which sell to the United States and the European Union had higher awareness of timber legality regulations and were more likely to take steps to eliminate illegal products from their supply chain by sourcing raw materials from the U.S. and Canada or by increasing their use of certified wood. However, smaller firms were generally unaware of timber legality regulations and were much less likely to use certified products. Our findings suggest that the Vietnamese market has become segmented, with smaller firms using materials which are at a higher risk of containing illegal wood, and exporting to less regulated markets, such as China.

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 score0.082
Threshold uncertainty score0.645

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 science0.0000.000
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 head0.022
GPT teacher head0.269
Teacher spread0.247 · 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

Citations14
Published2014
Admission routes2
Has abstractyes

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