Estimation of import demand models for the pharmaceutical products in Australia
Bibliographic record
Abstract
This study estimates the Australian import demand models for pharmaceutical products from the Rest of the World (RoW) and 4 selected countries. The selected countries in this study are France, Germany, United Kingdom and The United States of America. A total of 5 import demand models are estimated side-by-side, based on both monetary and Quantity (QTY) values, giving in total 10 import demand models estimated. The import demand model estimated consists of 5 explanatory variables: Real Price (RP), Real Gross Domestic Product (RGDP) and three Dummy Variables, dummy variables for the June (DQ2), September (DQ3) and December Quarters (DQ4). This study finds that all 10 import demand models are significant. Further findings are that the explanatory variable RP is mostly significant and inelastic; the RGDP is mostly significant and elastic and that import demand in June, September and December quarters are in average lower than in the March quarter. In overall, these findings suggests that changes in the relative prices of pharmaceutical products affect relatively smaller changes in the demand of pharmaceutical products, that the changes in real income in Australia affect larger changes in the demand of pharmaceutical products and that the import demand of pharmaceutical products in June, September and December quarters is lower compared to the March quarter in average
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".