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Record W2213531608 · doi:10.5558/tfc2014-129

The effects of the 2008 Lacey Act amendment on international trade in forest products

2014· article· en· W2213531608 on OpenAlexvenueno aff
Patrick Bridegam, Ivan Eastin

Bibliographic record

VenueThe Forestry Chronicle · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsIllegal loggingBusinessLegislationInternational tradePrinciple of legalityLoggingPossession (linguistics)Agricultural economicsEconomicsForestryGeographyLawPolitical science

Abstract

fetched live from OpenAlex

Despite international efforts, illegal logging continues on a scale that is of global concern, with significant volumes of illegally harvested wood entering into international trade flows. Recently, major importers of forest products have implemented timber legality legislation prohibiting the possession and/or importation of wood and wood products that are of illegal origin. Drawing on bilateral trade data and using a quantitative, regression-based comparative case study methodology, the effects of the 2008 Lacey Act amendment on the international trade of forest products were evaluated. A data-driven method was used to create aggregate control groups for comparisons with countries affected by the policy. If the policy has been effective in reducing the volume of illegally harvested forest products being imported into the U.S., we would expect to see some unique differences in post-policy U.S. imports of wood and wood products from areas with high levels of suspicious wood in their supplies. Results from these analyses show few substantial differences in post-policy imports of wood products of suspicious origins into the U.S. However, the results suggest that the policy may be affecting wood imports by major exporters of finished wood products to the U.S.

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)0.000
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 score0.345
Threshold uncertainty score0.197

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.006
GPT teacher head0.222
Teacher spread0.216 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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