Parallel Imports: A Copyright Problem with no Copyright Solution
Bibliographic record
Abstract
Parallel Imports refer to the legal importation of products that have some form of Intellectual Property rights attached to them. These products enter in direct competition with the products authorized for the imported market. As a result of that, Intellectual Property holders have attempted to deter these importations through the enforcement of Intellectual Property rights (such as Trademarks and Copyrights).\n\tIn this work, it will be shown that Copyrights cannot be used to prevent Parallel Imports. Copyrights grant the right to reproduce works of authorship and in that form to obtain a benefit from their first sale. Copyrights do not grant protection beyond that first sale making them unsuitable to halt the importation of original products.\n\tBy studying the form in which other countries have managed the Parallel Importation problem, a solution will be given to this issue.
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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 | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.011 | 0.025 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".