Liberalization of parallel imports regulations and marketing strategies of international corporations.
Bibliographic record
Abstract
This article examines two developments that challenge the classical model of diffusing products abroad through an exclusive distributor in each foreign country: the growing tolerance of many states for parallel imports, also known as grey products, and the rapid rise of international internet selling. It explains how price differences for identical products across national markets create incentives for parallel trade and presents the main objection to it, that grey marketers free ride on the investments of official distributors in building brand reputation and goodwill. The article distinguishes between the issue of consumer confusion, where courts require buyers to be informed of differences in product, warranties or services, and the issue of the legitimacy of parallel imports, which turns on whether the exhaustion of intellectual property rights applies nationally or internationally. It notes that no international agreement settles this question and reviews developments in the United States, where few obstacles remain to the entry of genuine parallel imports, and in the European Union, where exclusive distribution within the Union has been treated as a hindrance to competition. Cross-border online shopping is shown to intensify the phenomenon, calling into question the traditional model of exclusive foreign dealerships. Finally, the article discusses a possible shift by distributors from a 'pull' to a 'push' strategy that relies on retailers and other intermediaries. Abstract prepared by the editorial office from the full text of the article; the authors did not supply one.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".