Analysis of the log import market and demand elasticity in China
Bibliographic record
Abstract
Using country-specific data from 1992 to 2012, we estimated the demand elasticity of the log import market using the source-differentiated Almost Ideal Demand System (AIDS) model, the Error Correction Model (ECM), and both models in combination (ECM-AIDS), considering imports from Australia, Canada, Indonesia, Malaysia, Myanmar, New Zealand, Russia, and the United States. Regardless of which model used, the expenditure elasticity values were mostly positive, indicating a positive correlation between import volume and total import expenditure. Self-compensated price elasticity was negative, indicating that logs from all countries except Malaysia are relatively more sensitive to price, while import volumes from these countries are less sensitive to price. Cross-price elasticity values calculated using the static AIDS model showed that logs imported from Malaysia, Myanmar, and Russia are mutually complementary with logs imported from the other countries. Logs from Australia, Malaysia, and Indonesia; Canada and Indonesia; the US and New Zealand; and, Myanmar and Indonesia are mutually replaceable. The dynamic AIDS model found the same pattern regarding supplementarity, but indicated that logs from Australia, Canada, and Indonesia; the US and New Zealand; New Zealand and Indonesia; and Myanmar and Indonesia are mutually replaceable.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".