假著作權保護之名，行市場限制競爭之實—以加拿大最高法院Euro-Excellence
Bibliographic record
Abstract
商業競爭激烈，企業常利用法律之名打壓競爭對手。尤其在「真品平行輸入」的情況下，假著作權保護之名而行市場限制競爭之實的案件層出不窮，更彰顯以著作權限制競爭的爭議性。針對專屬被授權人可否以商品包裝之標籤著作物禁止真品商品平行輸入到加拿大的爭議，加拿大最高法院在Euro-Excellence Inc. v. Kraft Canada Inc. 案判輸入商勝訴。本文介紹Euro-Excellence Inc. v. Kraft Canada Inc. 案，並彙整加拿大最高法院之見解，接而藉由比較法方式探討我國相關規定與實務見解，最後提出本文之建議以供日 後我國司法實務判決之參考。 With stiff business competition, companies often try to suppress competition in the name of the law. To prevent parallel importation, companies increasingly sue their competitors for copyright infringement for the purpose of suppressing the competition. For an issue of whether an exclusive licensee can enjoin an unauthorized importation of genuine goods to Canada for sale, the Supreme Court of Canada in Euro-Excellence Inc. v. Kraft Canada Inc. ruled in favor of the importer. This paper discusses the outcome of Euro-Excellence Inc. v. Kraft Canada Inc. and addresses key issues of “copyright holders’ legitimate economic interests” and “incidental copyrighted works to the consumer goods”. This paper further explores similar issues and cases of parallel importation in Taiwan and provides the author’s recommendations for future legislative reference in regulating grey market goods in Taiwan.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".