Bibliographic record
Abstract
This chapter explains how goods enter into countries through customs supervision, and how certain kinds of goods are regulated for exportation. It begins with a brief history of import regulation, explaining how customs law has served taxation, national security, and foreign policy goals. It then walks readers step-by-step through the process of customs importation and determining whether and how much customs duties are owed, using real examples. In the process, it discusses the international treaties harmonizing customs classification, valuation, country-of-origin determinations, and marking. It then summarizes the various means by which importers can delay or avoid customs duties, such as the use of foreign trade zones or customs drawback. This chapter also discusses the history of export regulation and the major international regimes coordinating state regulation of exports for national security and foreign policy reasons. It walks readers step-by-step through the process of determining whether an export license is required for general (“dual-use”) goods and technologies; for military goods, services, and technologies; and for countries and consignees subject to trade or economic sanctions. Finally, it discusses the concept of trade boycotts, and introduces the antiboycott measures adopted by the United States, as well as legislation adopted in Europe and Canada in reaction to U.S. sanctions. It concludes with practice essays and multiple choice questions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".