IPP introduces additional Locality of Origin import price indexes
Bibliographic record
Abstract
The International Price Program (IPP) of the Bureau of Labor Statistics, as the primary source of data on price changes in the foreign sector, publishes monthly indexes of import and export prices for U.S. merchandise.1 While such indexes convey price information across product categories of goods traded between the United States and the rest of the world, there is evidence that price trends further vary by the geographic source of the product being traded.2 U.S. Locality of Origin (LOO) import price indexes were first published by IPP in 1992 for the following groupings, geographic regions, and countries: industrialized and other countries, Canada, European Union, Japan, and the Asian Newly Industrialized Countries (NICs); in 1997 the Latin America locality was added to publication.3 Since 1992, other countries and regions such as China and Mexico have emerged as important trading partners with the United States. Thus, in January 2005, price index series for these two countries were added to the set of published LOO price indexes along with six other localities: France, Germany, the United Kingdom, the Pacific Rim, the Association of South East Asian Nations (ASEAN), and the Asia Near East.4 (See box.) The new localities were determined according to customer interest, having a sufficient number and variety of usable item prices to reflect the actual dollar value and type of trade, and
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.027 |
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".