A categorical modeling approach to analyzing the impacts of the Lacey Act 2008 amendment on Chinese companies’ export cost and the implications on their sourcing behaviors
Bibliographic record
Abstract
The United States (US) Lacey Act 2008 amendment (LAA) is a timber legality regulation that requires US importers to monitor and minimize the risk of illegally harvested wood products within their supply chains. This paper empirically examines the effect of the LAA on Chinese companies’ export costs to the US. The study uses 138 responses from two surveys in Shanghai, China, in 2013, i.e., 5 years after the LAA was implemented. Given the high proportion of zero export increase indicated by the Chinese companies, a zero-inflated ordered probit model was used to model Chinese companies’ export cost increases to the US. The research results demonstrate that pre-LAA raw material sourcing patterns are primary indicators of the respondents’ export cost increase to the US as a result of the LAA. From the results, it can be inferred that log and lumber importers from suspect regions are taking additional measures, by changing their procurement practices, to ensure the legality of their raw material, which is adding to their cost structure. The results also indicate that smaller companies, given their flexibility with raw material procurement, were less likely to experience a post-LAA cost increase relative to their larger counterparts.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".