Intellectual Property Rights Protection in International Investment: Legal Risks and Strategic Responses for Multinational Companies
Bibliographic record
Abstract
Against the backdrop of ongoing globalisation and the rapid development of the digital economy, the protection of intellectual property rights (IPRs) has become a key factor in the overseas investment decisions of multinational companies (MNCs). This paper first reviews the evolution of international IPR regimes and their essential status in investment agreements. It then takes the Eli Lilly v Canada case as a core example to deeply analyse three significant difficulties currently faced in the protection of IPRs in cross-border investment: first, the uneven enforcement of laws across countries, which leads to inconsistent effectiveness in IPRs protection; second, the vague, outdated, and insufficiently adaptive provisions in existing investment and trade agreements, which fail to cover emerging technological fields effectively; third, the divergence between the application of international agreements and domestic legal systems, which increases legal uncertainty and compliance costs for multinational enterprises. Finally, the paper puts forward recommendations from both state and corporate perspectives, including strengthening international cooperation, improving the dynamic adjustment mechanisms of agreements, and urging enterprises to establish localised IPR strategies and compliance management systems. The article emphasises the need to construct a more coordinated, efficient, and forward-looking international IPR governance system to balance the protection of innovation with national sovereignty and corporate interests.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".