Legal and Economic Strategies for International Intellectual Property Protection: The Case of Software
Bibliographic record
Abstract
ABSTRACT. Intellectual property is an important asset for business and society. In 1998, the worldwide software market was estimated at $135 billion. Piracy, however, is reducing profits, innovation, investment, and tax revenues. In order to curb piracy, international intellectual property protection must be improved. This paper analyzes the Trade Related Intellectual Property Rights Agreement and the World Intellectual Property Organization. It also examines other methods of intellectual property protection, including arbitration, Digital Rights Management Systems, and price discrimination. Evidence suggests that optimal protection includes a mixture of international laws, pricing strategies, and governmental intervention. I. The Importance of Intellectual Property Rights Intellectual property is an increasingly important asset for businesses and society as a whole. It is a key element in the globalization and growth of the world economy. Software is a key component of this growth. In 1998, the worldwide software market was estimated at $135 billion. The United States captured seventy percent of global software sales [Gopal and Sanders, 2000, para. 1]. Piracy, however, is an increasing problem for the software industry. Not only is piracy wrong from a moral standpoint, but it also diminishes revenues and reduces incentives for investment in research and development. Software piracy is responsible for lost jobs, wages, and tax revenues. It also creates a potential barrier to success for software startups around the globe. According to the Software Publishers Association, piracy losses for the worldwide software industry topped $12.2 billion in 1999, for a cumulative total of $59.2 billion lost over the preceding five years. Losses were greatest in the United States and Canada, where they exceeded $3.6 billion [Software Protection Agency Report, 2000, para. 1]. Intellectual property protection will not only curb the software industry’s monetary losses, but it will also help society in the long run by enabling growth. Incentives for innovation will rise as innovators are rewarded for their creativity. Investors in research and development will also experience decreased risk as private property rights are enforced.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".